AEC September / October 26

Building Information Modelling (BIM) technology for Architecture, Engineering and Construction

EXCLUSIVE

Motif: agent-native BIM One database, one platform, unlimited agents — Motif’s answer to Revit’s ageing architecture

AEC compliance software

Modelling with GPT-6 Astra

Autodesk’s lofty AI ambitions

Navigating building codes & regulations

How can it fit into your design process?

...and the importance of context

September /October 2026 >> Vol.145 p01_AEC_SEPTOCT26_Cover.indd 2

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Building Information Modelling (BIM) technology for Architecture, Engineering and Construction

editorial

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Industry news 6

Compliance software 48

Endra automates electrical design, Lumion tackles architectural diagrams, Cupix looks beyond construction, and lots more

We look at the growing wave of AI tools that help AEC professionals navigate complex building codes and regulations

AI in AEC news 17

AI’s next bottleneck is the engineer’s desk 52

QA/QC tool links models and drawings, AI supports faster decisions on site, Rayon raises €10m for agentic AI, and lots more

Nomic is helping Arcadis automate engineering reviews, but AI’s next bottleneck may be the checking

GPT Astra and the future of BIM modelling 18 Giving AI actual OpenAI’s new AI model shows promise as write access in Revit 56 a 3D modeller, but how might it fit into a design process subject to constant change?

Motif: the agent-native BIM platform 26 We explore how this Revit challenger puts agents at the heart of modelling, documentation and collaboration

BIM 2.0: unfinished business 36 The BIM 2.0 startups might still have to catch up with Revit, but “come back when they’re finished” misses the point

Can AI move past querying Revit models and start building inside them? Chun-Lin Peng tests Claude + MCP + pyRevit

Revizto: putting the ‘I’ in BIM to work 59 Revizto’s MCP server is packaged with reusable Claude skills modelled on how project teams actually work

Qonic edges towards BIM authoring 62 Users of the powerful browser-based BIM platform can now start to build complete models from scratch

Autodesk’s most ambitious AI play yet 40 Drawing power 64 Autodesk’s AI vision goes beyond faster tools: a connected Assistant that decides how to solve tasks for you

Richard Harpham on why automating the production of drawings is only the beginning of the prize September / October 2026

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News

Endra unveils Power Studio for electrical design using AI

AMD to buy World Labs for $8.2bn MD has agreed to acquire World Labs, the spatial AI company, for approximately $8.2bn, with the deal expected to close by the end of 2026. World Labs develops AI models designed to understand, generate and simulate 3D environments, with applications in areas including design, gaming and robotics. AMD says the acquisition will help it shape its hardware and software around the demands of emerging AI models. Potential applications for AEC include design viz and reconstructing spaces from photographs. World Labs’ Atlas model can produce point clouds and gaussian splats, though the company acknowledges that it invents plausible content where source images leave gaps. Autodesk invested $200m in the company in February, as part of a $1bn funding round that also included AMD and Nvidia. The sale should deliver a substantial financial return on that stake, but the investment was also strategic. At the time, Andrew Anagnost, Autodesk CEO, described it as a “close collaboration at the research and model level,” intended to strengthen Autodesk’s own AI capabilities. What happens to that collaboration under AMD’s ownership remains to be seen.

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EP software developer Endra

months using conventional workflows.

Firms can capture design standards — launched Power Studio, an M has including equipment placement, AI platform for electrical engineering. The Stockholm-based company has also acquired Swiss mechanical engineering AI start-up Planlabs as it sets out to cover all three MEP disciplines — mechanical, electrical and plumbing. Power Studio allows engineers to load a building model, set project requirements and use software agents to carry out design tasks, with review and approval at each stage. Endra claims the software can complete the electrical design of a 500,000 sq ft commercial building in less than a day, compared to around two

circuiting, code interpretation and calculation methods — in configurable ‘Playbooks’. These standards guide tasks such as placing equipment, developing routing strategies and updating designs when architectural models change. Outputs include Revit models, single-line diagrams, shop drawings, panelboard schedules and bills of materials. Meanwhile, the purchase of Planlabs gives Endra technology for automatically generating mechanical system designs and checking them against physical constraints. ■ www.endra.ai

Graphisoft unveils new BIM and AI tools raphisoft has unveiled Archicad 30 which includes a host of modelling and documentation updates, alongside an expanded AI roadmap, a new subscription tier and integration with the forthcoming Nemetschek Creator platform. Archicad 30 includes improved visibility of walls across multiple storeys, more flexible documentation controls and expanded drawing label options. There are updates to Project Info management, and new customisable Navigator organisation, targeting the tasks involved in managing and documenting BIM projects. Meanwhile, forthcoming new AI features include an MCP Server and an updated AI Visualizer, with a choice of

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image generation models such as Nano Banana Pro. Elsewhere, an AI Assistant, which supports product learning and guidance on best practice, is also on the roadmap, while AI Assistant Spaces will allow architects to build a personalised knowledge base using their own docs. Graphisoft also highlighted Nemetschek Creator, a forthcoming platform for early stage design, which includes sustainability and feasibility insights, AI assisted analysis and connections to Archicad. Graphisoft is also expanding its subscription range with Archicad Max, which is aimed at practices seeking broader access to AI, visualisation, design intelligence and connected workflows.

odu Bridge is a new platform that moves BIM models from Archicad to Revit “without losing parametric intelligence.” Nodu inspects parameters and maps Graphisoft attributes to Autodesk Shared Parameters.

■ www.graphisoft.com

■ www.nodu.build

■ www.worldlabs.ai

Archicad to Revit bridge N

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08/10/2026 16:13

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ROUND UP ViaBIM tools

Motif introduces agentnative BIM platform

Via BIM, a specialist in BIM consulting for data centres, has launched a bundle of 17 Revit addins. According to the company, each tool is built around how real Revit teams actually work and cover the coordination and documentation workflows that take the most time ■ https://apps.viabim.co

Insights into Revit TwinMaster’s Arch-e, an AI assistant that helps project teams make informed decisions on cost, carbon, energy, compliance, daylight, comfort and constructability, now integrates with Revit and Forma Data Management. Optimised designs from Arch-e can be pushed directly back into Revit ■ www.thetwinmaster.com

otif, the software startup led

Spatial scanner XGrids has unveiled the Lixel L3, a handheld spatial scanner that combines LiDAR, true-colour imaging, Real-Time Kinematic (RTK) positioning and a built-in display. The device can capture true-colour point clouds, detailed meshes and photorealistic gaussian splats in a single workflow ■ www.xgrids.com

Asset partnership Glider Technology and Virtual Helpdesk (VHD) have announced a strategic partnership to help asset owners maintain accurate, trusted and auditable building information throughout the operational life of their estates ■ www.glidertech.com

Outlook to Forma At Autodesk University IMAGINiT previewed a new Microsoft Outlook integration with Autodesk Forma designed to enable project communications and information to flow more seamlessly from Outlook into the project environment ■ www.imaginit.com

$12m for Buildcheck Buildcheck has secured $12m in funding to accelerate the development of its design review and collaboration platform. The software can turn a contractor’s QA/QC playbooks into custom AI review agents, isolate changes between drawing revisions, identify code issues, and more ■ www.buildcheck.ai

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former Autodesk co-CEO M by Amar Hanspal, has unveiled

Motif Design, which it calls the first agent-native, open and collaborative BIM platform. Motif says its platform was built from the ground up for AI, where agents handle routine work such as layout, scheduling, drafting and documentation, by capturing and applying specialised expertise within a firm. The platform streams live from Revit

and Rhino without exporting or converting files, and it supports IFC and other formats. Multiple disciplines can work in one shared, cloud-based model at the same time, alongside agents. Motif describes the software as genuine parametric BIM, so if a user moves the core, reworks a floor plate, or updates a floor plan, then every plan, section, model, and sheet ‘updates instantly.’ Turn to page 26 for an in-depth look at Motif Design. ■ www.motif.io

Procore to acquire DroneDeploy rocore has entered into a definitive agreement to acquire DroneDeploy, the reality capture and visual intelligence platform, for approximately $845 million in cash. DroneDeploy’s platform spans drone, ground robot, mobile, fixed and wearable camera capture, processing imagery into

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3D site documentation. The company says its technology is used on more than three million jobsites across 180 countries. Procore, which is publicly listed on the NYSE, provides construction management software encompassing project documentation, collaboration and workflows. ■ www.procore.com ■ www.dronedeploy.com

Buildots adds laser scanning to platform uildots has added laser scanning to its construction intelligence platform through a partnership with reality-capture specialist NavVis, bringing ‘surveygrade’ precision to the system. The company is positioning the capability at mission-critical and mega-

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projects — data centres, semiconductor fabs and hospitals among them — where dimensional tolerances are tight and as-built accuracy has to be verified rather than assumed. Buildots already builds a visual record of construction progress, largely from 360-degree imagery. ■ www.buildots.com

www.AECmag.com

08/10/2026 16:13

News

Autodesk extends AI reach with standalone Assistant

Kora Studio launches for façade design ora Studio is a new design and modelling plug-in for Revit that enables architects to design a façade and immediately check whether it can be manufactured, transported and installed, according to its developer. Façade panel designs include grids, cladding and windows. As the architect models, each façade panel is checked against production data. The company says a complex parametric family that would traditionally take two days to build can be created in about five minutes using Kora Studio, while compliance checks that can take nearly two months are carried out instantly. The developer says the software works with any prefabricated or unitised façade system.

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utodesk is extending its agentic idea is that it will sit as an orchestration layer that surfaces issues before the user asks, pulls information from across a project, analyses how changes ripple products, projects and teams, while through schedules and designs, and routes drawing on connected project data in the right tool or agent to the right task. platforms like Autodesk Forma to Autodesk’s AI strategy is built around understand not just the question being context, understanding the specialised asked, but the project behind it. constraints of real-world projects, “It’s At AU, Autodesk previewed a new geometry, engineering intent and project agent-first standalone Assistant that it believes will enable a much larger group of history,” said Autodesk’s Mike Haley. people to find value in the engines and the Learn more on page 40. AI tools that Autodesk is building. The ■ www.autodesk.com/ai technology beyond A AI individual tools to span

Illoca brings ‘vibe modelling’ to AEC lloca has launched the beta of Plamo, an agentic 3D modelling workspace that turns sketches, reference images, drawings and natural language into editable geometry. The company describes the product as bringing “vibe modelling” to architecture and engineering — an echo of vibe coding, where developers direct software through

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intent rather than writing every line. Illoca argues designers still spend too much time manually building, rebuilding and coordinating geometry as projects evolve, and that agents can handle more of this work while designers direct the process. Plamo is available in beta, with 1,000 credits included in its free starting allocation. ■ www.illoca.com

vBrief takes control of project information Brief has re-engineered its AEC platform to help teams keep track of project-critical information, turning it into what the company describes as a verified, traceable knowledge base. CEO & co-founder James Wright-Zhang describes the software as a “modern

V

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practice notebook” — a place to record the information needed to operate a project. But unlike a traditional notebook, he says, the platform structures that information in a more digitally useful way. Each piece of information is linked to its source document and change history. ■ www.vbrief.ai

■ www.kora.studio

Meta unveils lightweight VR Glasses eta has announced Meta VR Glasses, a glasses-style VR device that will go on sale in spring 2027, priced at $1,300. At around 100 g, Meta says it is five times lighter than the Meta Quest 3. Most of that saving comes from splitting the hardware in two: the glasses hold the sensors and displays, while compute, battery and storage sit in a separate puck, connected by an optical tether. For AEC, removing weight, heat and the effort of putting on a headset could help break down some of the practical barriers to using VR for design review.

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■ www.meta.com

/vr-glasses

September / October 2026

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News

Lumion unveils Axogram for architectural diagrams

Volve targets tendering and pre-construction olve, an AI platform for tendering and preconstruction, has added an orchestration layer designed to automate more complex tasks across calculations, estimating and the interpretation of drawings and BIM models. Users can now give the system a task in natural language — for example, to “check that the quantities in the specification match the drawings and the IFC model”. Volve says the platform can determine the steps required, run those checks across a tender and verify the results before returning its findings, with the source behind each finding.

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umion has launched Axogram,

graphics software and repeating the whole

directly from their CAD or BIM models, without exporting views and redrawing them elsewhere. Diagrams are a staple of architectural communication, used to show how a project works, how people move through it, and why key decisions were made. According to Lumion, producing them has been an inefficient process that usually meant exporting fixed views, rebuilding them in

It runs alongside SketchUp, Revit and Archicad, so diagrams stay connected to the model as it evolves. Users can test conceptual styles without touching the original geometry or materials, and add arrows, labels, zones and styles directly around the model, in 3D. It can be used to produce different types of diagram to show sequence, context, section, and circulation.

process each time the model changes. new tool that lets architects L abuild Axogram is meant to remove that cycle. and update 3D diagrams

■ www.lumion.com/product/axogram

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families, checking the model, tagging and dimensioning, building views and sheets. Runs can be saved as a command for the whole team to reuse. New agents cover Revit families, bathrooms, MEP Systems and rebar modelling. ■ www.bimlogiq.com

■ www.iesve.com

how they relate to a project or workflow. The Context Layer creates a map of relationships across an organisation’s content and systems, giving AI agents relevant business context before they begin a task. Egnyte said this can reduce redundant processing and token costs while improving the accuracy and relevance of AI-generated results. ■ www.egnyte.com

BIMlogiq expands AI agents for Revit IMlogiq has enhanced Argus, its AI platform for Revit that uses specialist agents to automate a range of Revit workflows. According to BIMlogiq, users give it a goal and it plans the steps, chains the tools, and executes end-to-end: placing

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IES building performance links to Revit ES has unveiled IES Sync for Revit which offers a native connection between Revit and its building-performance simulation platform IES VE. Rather than exporting an IFC or gbXML file, or reconstructing a model in IESVE from a 2D DXF file, IES Sync connects Revit directly to the IES ecosystem through a shared data ‘intelligence layer’ that carries geometry, including voids and air plenums, and, in future versions, energy-relevant data. The company has also launched IES VE2026, a ‘major update’ that is said to cut repeated set-up, checking and rework from everyday analysis while retaining the building physics and technical depth required for complex projects across key HVAC design, data centre, compliance, and BIM to BEM workflows.

Egnyte gives AI agents key context gnyte has launched a new Context Layer designed to help AI agents better understand how a company’s content, people, projects and business systems connect. The company said the technology addresses a key problem for businesses with large volumes of complex content: AI can search documents, but often lacks the wider context needed to understand

■ www.volvetech.com

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www.AECmag.com

08/10/2026 16:13

AI AGENTS FOR DESIGN, ENGINEERING, AND CONSTRUCTION

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ROUND UP Project intelligence

Cupix looks to move beyond construction into operations

ForgePlan has launched a “construction intelligence platform” that uses AI to analyse relationships across interconnected construction documents and identify potential issues, turning them into reviewable next steps, including correction items, RFI drafts and more ■ www.forgeplan.com

SLAM to Trimble Emesent now supports direct transfer of scanning and survey data from its high-accuracy mobile SLAM scanners into Trimble Connect and Trimble Business Center. According to the company, raw scan data becomes clean point clouds ready for BIM models, as-builts, and digital twin applications ■ www.emesent.com

Construction ERP Accenture has launched Accenture Construct, a new global business that aims to help owners plan, deliver and manage large infrastructure and capital projects, such as airports, rail networks, power grids, data centres and manufacturing facilities ■ www.accenture.com/construct

CivilGrid gets $26m CivilGrid, the US-focused civil design platform that consolidates site data into a collaborative map to give a comprehensive view of underground assets and conditions to help “builders deliver safe and affordable projects” has secured $26 million in Series A funding ■ www.civilgrid.com

Rail carbon insight Great Britain’s Rail Safety and Standards Board (RSSB) has launched the Carbon Management Tool (CMT), a rail-specific platform designed to help organisations understand, manage and reduce whole life carbon emissions from infrastructure and railway assets ■ www.rssb.co.uk

Scheduling link Outbuild has expanded its Autodesk Forma integration. Autodesk RFIs, submittals, and drawings are now connected directly to planning and scheduling workflows inside Outbuild’s platform ■ www.outbuild.com

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upix is repositioning

linking it to the broader information that

twin platform into what the company calls Enterprise Spatial Intelligence (ESI). The upcoming version 5.0 is intended to extend the platform across the asset lifecycle, from construction into operations and maintenance. Until now, CupixWorks was primarily associated with reality capture and digital twins for construction. Version 5.0 is intended to move beyond that record,

enterprise asset-management data. Cupix says the combined dataset can help teams align design, schedule and site progress, and automate comparison of actual conditions with project intent. Then by linking spatial records with asset data, construction history can be carried into operations to support maintenance and future renovation.

governs a project, including 360, drone, and from a C CupixWorks construction-focused 3D digital- laser scan data, BIM models, schedules and

■ www.cupix.com

Sherpa brings ‘prompt-free’ AI rendering herpa is a new AI rendering tool for SketchUp that works image-to-image on a snapshot of the user’s own design rather than text-to-image. With Sherpa, everything that would normally go into a prompt is instead a control: luminosity, time of day, colour temperature, artificial lights, whether

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people appear, styling level, and named per-surface materials. Because the controls sit on top of the same base image and camera, an architect can run one viewpoint through daylight, evening light, an empty version for handover drawings, a marble floor, and a different sofa — then compare all five results from a single export. ■ www.sherpa-render.com

Arcadis invests in AEC AI platform rcadis has made a strategic investment in Nomic, an AI platform built specifically for AEC workflows, and entered into a long-term commercial partnership with the company. The investment follows a six-month trial involving around 150 Arcadis

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engineers across multiple disciplines, sectors and countries. The trial tested Nomic’s AI agents on core delivery tasks including drawing review against firm standards, code compliance checks, submittal review, RFI research and BIM coordination. Learn more on page 52. ■ www.nomic.ai

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08/10/2026 16:14

News

HP ZBook Ultra G3a laptop brings local AI to Revit

Matterport embraces point clouds t Autodesk University 2026, Matterport demonstrated a new Point Cloud Import capability that allows users to bring an existing, registered E57 point cloud directly into a Matterport account, automatically generating a navigable 3D reality model. Once imported, the model can be explored and shared as a Matterport “digital twin.” Users can open it in a browser on any device, move through the space, take measurements, add contextual information and extract spatial data. People who were never on site can mark up conflicts, check clearances and leave questions tied to specific pieces of equipment. Matterport also showed workflows with Autodesk software. Forma, for example, can receive anchored RFIs and issues through a bidirectional plugin, while Matterport also provides direct workflows into Revit and Autodesk Tandem. Matterport allows you to lock the registration and preserve the geometry contained in the E57 during import. The survey-grade point cloud, typically captured with a Leica, Faro, Trimble, or NavVis scanner, retains its existing registration.

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P is pitching its new ZBook

160 GB of which the GPU can use. That is far more memory than a typical discrete laptop GPU, and it’s what allows HP to claim that the system can run AI models of BIM, visualisation and simulation. It up to 300 billion parameters locally. calls it a “personal mobile token Perplexity’s Computer platform is factory” that runs AI agents and LLMs locally, and through a partnership with central to this story, allowing LLMs such Perplexity, those agents can query Revit as Qwen or Nemotron to run entirely on the Ultra G3a. Agents can interrogate BIM models via Revit 2027’s MCP server. models and other documents locally, The key feature is the AMD Ryzen AI without sending sensitive project data to Max Pro processor, which puts the CPU, GPU and NPU on one chip with fast access the cloud or consuming tokens. to up to 192 GB of shared memory, up to ■ www.hp.com G3a as more than a H Ultra mobile workstation for CAD,

HP improves construction automation P has added new capabilities to its SitePrint robotic layout system and Build Workspace construction drawing management platform, to help speed document workflows and reduce on-site rework For HP SitePrint, the headline addition is a new cloud-based CAD editor. Previously, layout jobs had to be prepared in AutoCAD

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or Revit and uploaded into the robot’s workflow. Now operators on site can make last-minute corrections themselves. Build Workspace now includes enhanced version control and comparison tools, and scannable QR codes that show if a paper drawing is current and provide direct access to any subsequent revisions. ■ www.build.hp.com

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Faro unveils new scanners

into the Forma industry cloud, while Revit continues as a “Forma Connected Client” and a bridge to what comes next. Autodesk promises no sudden transition and says investment in Revit will continue, but the direction is now on the record rather than buried in a roadmap.

aro has introduced two new laser scanners, the Focus i.1 and i.1 X, which are said to combine enhanced hardware, improved measurement performance with object scanning accuracy up to 0.9 mm, faster colourised scanning, and advanced positioning technologies.

■ www.autodesk.com

■ www.faro.com

Forma will replace Revit ‘over time’ utodesk has said more plainly than ever where Revit is heading. Asked in its Autodesk University press release FAQ whether Forma will replace Revit, the company answered: “over time, yes.” Design, analysis, coordination, and documentation will move progressively

■ www.matterport.com

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News

Formas.AI unveils Cartesian, a native agentic 3D modeller

Vectorworks 2027 boosts visualisation ectorworks 2027, the latest version of the CAD/BIM software, introduces new tools aimed at simplifying workflows and improving visualisation, documentation, coordination and information exchange. The release is said to deliver faster, more realistic visualisation through modernised physically based rendering (PBR). Direct GPU support using Vulkan and Metal is also designed to improve 3D navigation, with Vectorworks reporting performance gains of up to 15x to 35x for tasks such as panning and zooming. AI Visualizer now supports Google’s Nano Banana AI models, allowing designers to explore concepts and generate high-res images within the software. The new Exchange hub provides access to shared design resources, while expanded Revit Families and DXF/DWG Dynamic Blocks support improves content interoperability. Other updates include enhanced architectural phasing, doors and windows workflows, plus more accessible and accurate georeferencing tools.

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ormas.AI has unveiled

available tools, construct solids and freeform surfaces, and inspect and revise geometry programmatically. Models can be organised into named components, architecture, interiors, furniture and layers and assemblies with relationships product design that aims to turn that help describe how parts belong AI-generated concepts into editable, together. Co-founder Carlos Bañón said physically meaningful geometry. The company argues that AI can already these relationships “give geometry meaning and help preserve design intent produce convincing images of buildings and objects, but turning those images into as the model changes.” An orchestration system coordinates models raises harder questions about how multiple AI models, including OpenAI’s parts fit together, which constraints GPT-6 Astra and Claude Opus 5.5, matter and what should change as a alongside specialist agents across design evolves. Cartesian lets designers reference interpretation, brief start from photographs, sketches, written development, geometric authoring and briefs or existing models, then refine review. Parallel improvements are individual components through merged through geometry checks within conversation, carrying requirements and the shared model. Integrations with Rhino references into each revision. and Blender complement the system with At the platform’s core is a proprietary additional CAD and mesh workflows. CAD kernel layer and geometric toolkit built for AI agents. Agents can discover ■ www.formas.ai an AI-assisted F Cartesian, platform for early-stage

Pirros adds QMS to Revit-centric platform irros has added an AI-powered Quality Management System (QMS) to its AI project hub, a platform that allows architecture and engineering firms to find, compare, reuse, and standardise Revit details and families across projects, teams, and offices. The new QMS allows users to review an entire Revit project throughout design development — against codes, firm standards, project specifications, and model performance.

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Any issues that are found can then be assigned to an individual, tracked, and resolved before they become RFIs or rework. Issues can also be assigned to Mira, the Pirros AI agent, to make the fix directly in the Revit model. Findings are never auto applied According to Pirros, its QMS is different to most QA/QC drawing checkers, which work with exported drawing sets and the user inputs any findings into Revit by hand. ■ www.pirros.com

■ www.vectorworks.net

SketchUp target civil contractors ketchUp Pro Civil is a new S Contractor tool designed to make it easy to create 3D earthwork models in SketchUp and bring them into Trimble Siteworks and the Trimble Earthworks grade control platform. Trimble says it simplifies the move from design to machine guidance without the complexity of traditional 3D CAD software. ■ www.sketchup.com

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Accelerate Innovation with Max Performance Experience unmatched performance with HP Z workstations accelerated by NVIDIA RTX PRO™ GPUs1 designed specifically forarchitecture and engineering professionals. Achieve faster designs, smooth real-time rendering, and visualization at any scale.

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AI NEWS BRIEFS MCP for Cinema 4D Maxon has added Model Context Protocol (MCP) support to Cinema 4D, enabling artists to use natural language to help accelerate Cinema 4D workflows using AI services such as Claude or ChatGPT ■ www.maxon.net/cinema-4d

Sponsored by

QA/QC platform links BIM models and drawings

Code compliance OFA Group has launched PlanAId, an AI-assisted drawing review platform designed to help AEC firms identify potential building-code issues while plans are still in development. Users upload PDFs exported from AutoCAD ■ www.planaid.ai

Construction ERP CMiC has added new capabilities to its AI-powered construction ERP platform, NEXUS, that are designed to bring automation deeper into construction workflows such as job costing, budgeting, project setup, field reporting and change management ■ www.cmicglobal.com

26ers target AEC 26ers has launched an agentic AI systems studio to help AEC firms build what founder and CEO Theresa Piasta describes as “useful AI systems, with human oversight and judgment at the centre” ■ www.26ers.ai

Quantity takeoff BOQIFY is a new AI-powered construction takeoff platform that automatically analyses architectural and engineering plans, extracts quantities, and generates structured Bills of Quantities ■ www.boqify.ai

SPOTLIGHT

tructured AI, the AI-powered

platform for drawing S QA/QC packages, can now bring BIM

models and the documents around them into a single review. Design teams have typically checked models in one tool and drawings in another, with specifications, schedules and manuals reviewed by hand in between. Structured AI instead lets teams load BIM models, as IFC or native Revit files, alongside PDF drawing sets, specifications, codes, client and project manuals and schedules. Its AI agents then check them against each other.

A drawing can be checked against its specification, schedule or governing code, and any issues found on a sheet can be traced to the affected element in the model. Structured AI explains that every finding is cited to the exact location on the sheet or in the model, and all of them sit in one issues list, wherever they were raised. Inside Revit the same findings are opened in a pane and selecting one takes the engineer straight to the element concerned. The add-in can also run checks on the live model and propose fixes, which are applied only once approved. ■ www. getstructured.ai

AI helps make faster decisions on site penSpace has enhanced its Visual Intelligence Platform with new features to help construction teams understand, navigate and document jobsites in real time. The updates are said to move OpenSpace beyond traditional reality capture toward a broader vision where teams can use AI

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and spatial data not just to create a record of construction, but to make faster decisions while work is underway. New capabilities include AI Autolocation 2.0 with Live Location, Site Mode, AI Walk-and-Talk and AI agents that can see, understand, and act on job site data. ■ www.openspace.ai

directory

FIND YOUR AEC AI TOOL ■ aidirectory.aecmag.com

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Rayon raises €10m for 3D and agentic AI ayon, the cloud-based CAD tool known primarily for 2D design, has secured €10 million in Series A funding to expand into 3D and accelerate its AI development. Rayon Version 3 introduced AI tools for generating and editing images, tracing

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images into vectors, and turning product photographs into CAD blocks. Version 4, due later this year, will add 3D to its collaborative environment, along with generative tools and agentic AI capable of carrying out tasks within a project. ■ www.rayon.design

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GPT Astra and the future of BIM modelling AI has made a significant leap forward in working with 3D geometry. Open AI’s latest model, GPT-6 Astra, can turn drawings, photographs and instructions into 3D models. For AEC, the bigger question is how this capability will fit into a design process in which almost everything is subject to change, writes Martyn Day model and developing a detailed, accurate building model. The distinction becomes apparent when someone asks for a change to that model. The devil is in the detail: it’s not just the model’s visual fidelity, it’s how the building was modelled. At the moment, the model may reproduce the appearance of a building without any real idea of how it ‘works’. These demonstrations establish useful capabilities, but do not yet establish dependable judgement about circulation, clearances or construction.

Astra under test

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here is something dis- Nicolas Catellier, BIM plan modelling operations, Pure, experiments write code, inspect the result concerting about with using Astra to and make corrections. Tests in watching software create an apartment Blender, Rhino and Revit range attempt work that has layout in Revit from impressive reconstrucoccupied professionals for decades. Give Open AI’s new GPT-6 Astra tions to models that would make a BIM model references of building, connect it manager wince. AEC has spent decades investing in digto 3D modelling software and leave it to work, and geometry appears, sometimes ital building models, with skilled people looking more resolved than it is. Models translating design decisions into objects, once built object by object can now be parameters and relationships. Automating attempted through conversation or more of that work changes the economics of BIM production and potentially the uploaded drawings. Experiments by architects with way design software is used. There is, however, a considerable gap ChatGPT Astra deserve attention. A general-purpose AI can interpret references, between producing an impressive first

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Oliver Thomas, formerly design technology manager at BIG, rebuilt BIG’s Kaktus Towers in Blender, Rhino and Revit from reference images. Tim Fu of pioneering AI in architecture practice Studio Tim Fu, says his research team began experimenting the week Astra appeared, and his spin-off STF Intelligence is building agents for Rhino, AutoCAD and Revit, though no output is available in the material reviewed here. Fredy Fortich, a technical architect at MVRDV, is teaching a Rhino and Revit MCP course, while Marijn Luijmes of LEVS architecten and Daoru Wang at UMass Amherst have posted Grasshopper and Rhino tests. Timofey Lyutomsky of DK Razum is reported to have taken a three-minute sketch to a three-discipline house. Much of this early testing is being shared by individuals and small teams on LinkedIn and X. www.AECmag.com/AI

08/10/2026 15:53

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Astra experiments

and included furniture and surrounding Starting without drawings Nicolas Catellier of BIM Pure asked Astra objects. The difficulty was that it used A new client brief rarely comes with such to create an apartment layout from DirectShape geometry, leaving a result convenient inputs for AI to model from. scratch in Revit, through MCP, using a with little of the normal editing behaviour There is a site, an accommodation schedreal project by Quinzhee Architects as the expected from a Revit building model. ule and a client whose ambitions may not Through a Revit MCP server with full fit either. Working out what to build is comparison. In the first attempt, the AI put the fridge in the circulation path. The write access, a Russian Revit plugin devel- the job of the architect. To use AI auto second layout was improved by increas- oper, BIM Koordinator, reports Astra pro- modelling, the architect will need to proing the model’s effort level and supplying ducing 327 native walls, 162 doors and 98 vide sketches and descriptions. further instructions, but Catellier still windows in a 16-flat block. This was an In OpenAI’s architectural visualisation judged it inferior to the human-made unattended run from a single screenshot, example, Astra began with a written brief scheme. His test shows how readily an reported by the developer selling the plugin rather than a supplied floor plan. The proagent can produce geometry while miss- and without independent verification. ject developed through successive Ideatura’s reconstruction of the reviews, including a proposed plan and a ing fairly basic planning problems. Astra works through a software Leadenhall Building from drawings furnished Blender scene. OpenAI identiconnection, typifies it as a visualcally an MCP servisation project er, writing code or requiring profescalling tools inside sional review The architect who understands the problem may not be the modelling before it could able to code, while the computational designer has a queue application. Those inform construcof requests. An AI that can write and run modelling code tools determine tion. Each verwhether it producsion gave the could make smaller jobs worth automating es building eleuser something ments, generic to respond to. solids or meshes, The next and how much of the resulting model it shows why the software connected to instruction came from seeing what had can inspect and change. Give an agent Astra matters. Whereas BIM Pure’s cabin been built, rather than from a complete access to native walls, doors and levels came back as DirectShape geometry specification written at the outset. and it has a different set of options inside Revit, Ideatura created elements Consider a small housing scheme. The from an integration built around arbi- classified within IFC, so a structural architect supplies a site boundary, access member could be identified as a column arrangements and an accommodation trary geometry. Procedural modelling is hardly new to rather than simply displayed as a solid. schedule, then asks for alternative organReconstruction gives the AI a consider- isations. After reviewing them, the archiAEC. Practices have spent years using scripts, Rhino Grasshopper and able head start. Published drawings and tect draws a courtyard, moves an Dynamo to generate geometry and auto- photographs contain someone else’s entrance and reshapes one wing. The mate repetitive work. The constraint has architectural decisions, even when the agent is then asked to develop the accomoften been access to someone who can demonstration compresses all that work modation around those decisions, with build and maintain the routines. The into a single screenshot. assumptions kept visible. Catellier’s sketch-to-detail experiment architect who understands the problem may not be able to code, while the com- comes closer to everyday practice. He Augmenta and the ‘one shot’ problem putational designer has a queue of combined a rough sketch with office pro- Augmenta met this problem before Astra requests. An AI that can write and run cedures and existing detail components. arrived. Its routing engine solves a whole modelling code could make smaller jobs Astra asked questions before drawing, building’s electrical containment overthen produced work that needed correc- night, and customers accepted the result. worth automating. BIM Pure’s cabin experiment exposed a tions to framing, furring and component Then the design changed, and rather different limitation. Astra reconstructed a choices. Reviewing it required someone than run the whole solve again against recognisable building from photographs who knew how the detail should be built. the new design, they went back into

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Andrea Rocco Matta used Astra and Ideatura to reconstruct the Centre Pompidou in IFC from historic drawings and photographs. He directed the process, reviewed the results, and requested corrections

Revit and Navisworks and edited the output by hand, which is the rework the automation was meant to remove. In a June 2026 interview, CPO Aaron Szymanski described version 2 as built around that behaviour: adjust the affected routes, rerun only the areas that changed and leave the rest of the accepted solve alone. The engine works with project rules and geometric constraints, using algorithms and machine learning rather than an LLM to design the routes, but the lesson transfers to any automated modelling: the first result is only useful if the second change does not throw it away. An architect might ask an agent to model a floor, then move some walls and change the entrance by hand. When the agent resumes work, those edits need to become its starting point. If it returns to its original proposal, the architect must make the same corrections again. That would quickly undermine any saving from automated modelling. Ideatura’s Centre Pompidou reconstruction offers an example of staged development: the agent reports adding bracing, moving an escalator clear of it and retaining existing services and glazing. Matta acknowledges that some details still need verification, and the model files would need comparing to establish what survived the revision.

Where the manual work goes Repetitive placement, parameter changes and routine documentation look set to become automated first. Practices with standard components and clear modelling rules will have an advantage, as long as the resulting dimensions and annotations stay attached to the right information. Architects will still move geometry 20

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around to develop an idea, then ask the AI to deal with the repetitive consequences. They could end up spending more time exploring models while placing fewer objects themselves. Cheaper alternatives would make that easier to justify on a project budget. An awkward envelope junction will take more than a good modeller. Structural movement, water management, fire performance and construction sequencing all bear on the detail. An agent needs access to the relevant product knowledge and tested assemblies before its geometry becomes useful construction information. Agents could also prepare alternatives for analysis, run established tools and bring the results back to the designer. That would allow more options to be tested, but the assumptions need scrutiny. A simulation with the wrong boundary

conditions can make a poor decision look thoroughly researched. For now, the modelling runs are still expensive enough to count. BIM Pure’s detail experiment used roughly $10 in API-equivalent tokens, with an initial recreation taking about 20 minutes; the 16-flat block took 79 minutes at maximum settings. Elsewhere, a reported Rhino massing of the Heydar Aliyev Center in Baku consumed the user’s daily limit. These examples provide no matched comparison of total effort against the existing workflow. Briefing, checking, failed runs and repairs belong on both sides of the calculation, with the same standard of deliverable. Repetition may improve the saving, but the proportion of a fee spent placing objects is not the proportion an agent will necessarily remove. Document checking has the strongest measured result among these tests. In www.AECmag.com/AI

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Signal’s experiment, Astra found 13 of 14 planted defects in a 40-page set and produced 23 observations, 21 of which were confirmed. An engineer can review each discrepancy between a drawing and a schedule, or check a quantity that does not add up. The benefit is easier to establish than the value of a model awaiting an unknown amount of repair.

Tools built for hands Most BIM software was built for someone sitting at a workstation, placing and editing elements through commands, properties and dialog boxes. Nick Cameron, director of digital practice at Perkins&Will, told AEC Magazine that a large hospital has to be split into around 30 linked Revit models to remain workable. The practice then needs to coordinate those separate models as the design develops. Linking the files does not automatically give an AI access to everything in them. If its connector exposes only the active model, the agent works with a partial view of the project. A change to the architecture may have consequences in a services model it cannot even query. Faster modelling will only make that omission more expensive. As agents take on more of the work, the model environment must support repeated queries and coordinated changes across the project without requiring a person to nurse each operation through. There are already different approaches to the underlying data. That Open Company’s Fragments supports partial loading, querying and editing; Motif describes a distributed data platform; Snaptrude has a database stack for real-time updates; and Qonic’s API exposes project models, properties and edit sessions. That does not establish how any of these tools will perform through the design and documentation of a large hospital. It puts pressure on desktop BIM software vendors to explain how their systems will cope. An agent that can generate thousands of elements is of limited use if the BIM application makes it difficult to inspect their dependencies and revise them together. The capacity to do that reliably could become a deciding factor in where practices choose to model.

A different modelling environment The software vendors are taking different approaches to giving AI access to their professional BIM models, while practices are exploring their own options. Perkins&Will’s experiments with Blender for AI-driven modelling suggest that this work need not stay inside the established BIM applications. Much will depend on www.AECmag.com/AI

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how freely an agent can read and modify project information across tools. Vendors can make that easier, or use their control of those connections to keep more of the work inside their own products. While GPT-6 Astra is grabbing the headlines, the competition is not far behind, and there appears to be little reason for AEC firms to commit permanently to any single model. In BenchCAD, a benchmark for programmatic CAD, vendor-reported scores for mechanical-part geometry put Anthropic’s Opus 5.5and Astra effectively level (0.962 vs 0.959). The benchmark is not AEC-specific, but it suggests the gap between leading models is narrow, and open-weight models may widen the choice further. Adoption will probably start with well-defined work that a practice can specify and check. Standard components and established rules give an agent a better starting point than unresolved design decisions, whatever the building’s shape. This may eventually allow architects to develop detailed models from 2D sketches and written descriptions, with further decisions supplied as the design progresses.

Conclusion How will practices use this technology? They will need to decide whether to spend the saved hours exploring designs or taking on more work, while clients may expect lower fees. They will also have to reconsider training. If junior staff spend less time working through models and details, firms will need another way to teach the construction judgement required to review them. Start with reading tasks, keep the MCP connector read-only until its editing operations are understood, and check the licence terms before unattended automation. Choose one repeatable task and compare the office’s method with an agent working to the same acceptance criteria. Include a manual change and a second automated revision, checking that agreed decisions survive and counting all briefing, checking, repair and computing costs, including failed runs. Astra has put a substantial amount of manual BIM work within reach of automation. The first model might get the attention, but an architect needs to keep working on it for months. The software will have to remember what was agreed, accept direct intervention and carry those decisions into the next revision. The evidence reviewed here has yet to demonstrate that it’s reliable enough to establish a production saving.

AI spotlight Cover story

The Astra moment in engineering Brad Rothenberg, founder of nTop, has spent years developing modelling software for demanding additive manufacturing and aerospace engineering work. His verdict in a recent podcast was emphatic: “I don’t think it’s the ChatGPT moment. I think we’ll call it the Astra moment.” He tested Astra on a B-52 fuselage using public drawings. The first loft resembled the wrong aircraft. The second lost its smoothness and incorporated landing gear into the fuselage. With further direction, Rothenberg reports a smooth loft. Astra cut cross-sections, compared their areas with the real aircraft and added a checks section to the nTop notebook without being asked. Rothenberg was impressed with the accuracy. He also offered a useful qualification: “All the stuff that CAD systems are really good at, LLMs are really good at doing. All the stuff that CAD systems have trouble with, LLMs also have trouble with.” Easier access to those operations still leaves plenty of work for engineering judgement. Regulated-industry customers of nTop were impressed but could not use Astra or Claude Fable 5.1, he says. Rothenberg expects comparable open-weight models within six months, running on-premise. For AEC practices handling sensitive projects, that is a development to watch; his forecast supplies no deployment option today.

■ www.openai.com

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BOOSTING REVIT & AUTOCAD 2027 Autodesk AutoCAD 2027 and Autodesk Revit 2027 both embrace modern graphics technology, shifting more power to the GPU for ultra-smooth navigation of drawings and 3D models. Paired with the AMD Ryzen™ AI Max PRO processor, the latest HP Z Workstations deliver professional-grade performance for CAD and BIM in compact, energy-efficient form factors ®

®

®

Product features and availability may vary. For complete details & disclaimers, please visit www.hp.com/zbookultra

®

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AD and BIM software is entering a new era. Autodesk® AutoCAD® and Autodesk® Revit® — two of the most widely used design tools in Architecture, Engineering and Construction (AEC) — are now getting more out of modern graphics APIs and software frameworks, enabling them to fully tap into the power of today’s advanced Graphics Processing Units (GPUs). This shift offloads more of the processing from the CPU to the GPU, easing a common performance bottleneck. The result? Smoother model navigation, more responsive design environments, and a significant boost in productivity — with less need for model simplification or

performance workarounds. Meanwhile, workstation hardware is evolving just as fast. Traditionally, integrated graphics — where the GPU is built into the CPU — was often seen as inadequate for professional use. Discrete GPUs used to be the standard. But that assumption is now being challenged. AMD Ryzen™ AI Max PRO processors feature integrated Radeon™ GPUs with levels of performance once reserved for discrete solutions. These GPUs are not only powerful and energy efficient, but also fully compatible with, and optimized for, the advanced graphics features in AutoCAD 2027 and Revit 2027.

This convergence of next-gen software and hardware marks a turning point. Architects and engineers using some of the latest HP Z Workstations — such as the slimline 14-inch HP ZBook Ultra G1a and 16-inch HP ZBook Ultra G3a laptops and compact HP Z2 Mini G1a mini desktop — can now access the advanced graphics capabilities of Revit 2027 and AutoCAD 2027, without needing a discrete GPU. The benefits? Lower cost, reduced power consumption, and sleeker, more portable hardware. As software and hardware continue to advance in tandem, the future of CAD and BIM promises to be faster, smoother, and more flexible than ever.

Produced by

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REVIT: ACCELERATED GRAPHICS

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evit 2027 introduces a new graphics technology designed to significantly improve performance in both 3D and 2D views. ‘Accelerated Graphics’, which first appeared as a ‘Tech Preview’ in Revit 2026, delivers smoother, more responsive model navigation when panning, orbiting, or zooming. Those working with particularly complex Revit models should experience the most noticeable performance benefit. To unlock these performance gains, the Revit development team has harnessed the latest technology in both software and hardware. This includes Universal Scene Description (USD) — an XML framework born out of Pixar® but increasingly used in AEC for data exchange and collaboration. However, Revit uses USD in a different way. With Accelerated Graphics it’s employed solely to draw the graphics on screen, faster. A direct export of Revit models to USD is not currently on Autodesk’s roadmap. Accelerated Graphics also makes much better use of modern GPUs — processors

that are well-suited for graphics rendering. By offloading more of the graphics workload to the GPU, visual performance is significantly improved while also freeing up the CPU to handle other tasks — helping boost overall system performance. Accelerated Graphics in Revit supports a wide range of display styles including ‘shaded’, ‘hidden line’, ‘realistic’ and ‘transparency’. It also allows section boxes to update live, so users can cut through the model and see the section change in real time. Accelerated Graphics can be toggled on and off at any time. This is useful for certain workflows, since some elements, such as materials in realistic views, and some data types, such as point clouds, are not yet supported. To get the best performance Autodesk recommends a workstation with at least 64 GB of RAM and a GPU with at least 8 GB. In the past, this would have required a workstation with a discrete GPU. However, with the AMD Ryzen AI Max

PRO processor — featured in the HP ZBook Ultra G1a and G3a laptops and HP Z2 Mini G1a desktop workstation — users now have access to an integrated AMD Radeon™ GPU with the equivalent performance of a mid-range discrete GPU. Coupled with up to 128 GB of system memory (or 192 GB in the case of the HP ZBook Ultra G3a), of which up to 75% (G1a) and 83% (G3a) can be assigned to the GPU, the chip is fully capable of handling Revit’s new graphics demands. When running Revit’s Snowdon Tower sample project on the HP ZBook Ultra G1a with an AMD Ryzen AI Max+ PRO 395 processor, the viewport feels significantly more responsive compared to the traditional graphics engine. Model navigation is silky smooth, and the model is not simplified in any way — a technique often used to maintain full interactivity with larger models. As shown in our benchmarks below, performance (measured in frames per second) is between 1.5 and ten times faster.

Accelerated Graphics DISABLED

Revit 2027 Snowdon Towers Sample Architectural Project (Frames Per Second (FPS) - bigger is better)

Accelerated Graphics ENABLED

Simplify display during view navigation DISABLED Shaded

Hidden Line 15.5

2.4 23.4

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24.7 50% transparency

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Product features and availability may vary. For complete details & disclaimers, please visit www.hp.com/zbookultra

Snowdon Tower project from Autodesk® Revit® 2027

The HP ZBook Ultra G1a & G3a laptops and HP Z2 Mini G1a desktop with AMD Ryzen™ AI Max PRO processor, are well positioned to maximize the benefits of Revit 2027 and its next-gen graphics engine — delivering a significant performance boost, particularly when navigating complex models Snowdon Tower project from Autodesk® Revit® 2027

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Sponsored by HP / AMD

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Test machine: HP ZBook Ultra G1a with AMD Ryzen AI Max+ PRO 395, 128 GB memory (64 GB allocated to AMD Radeon 8060S GPU). Resolution 2,880 x 1,800 (150%).

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ADVANCING AUTOCAD

Bring your BIM models to life with advanced real-time visualization

AutoCAD 2027 feels faster and smoother when navigating 3D models, thanks to Graphics System Fabric (GSF), a powerful graphics engine built on DirectX 12 that harnesses the power of modern GPUs

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Heavy machinery model courtesy of Mastenbroek

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winmotion for Revit by Epic Games, included with select Revit subscriptions, brings high-quality, real-time visualization directly into your BIM workflow. The software is GPU-accelerated, and the AMD Ryzen AI Max PRO processor is well suited to handling mainstream visualization tasks. A key advantage is the ability to directly allocate up to 75% of the total system memory to the processor’s integrated AMD Radeon GPU — far more than most discrete GPUs with comparable performance. On test with the HP ZBook Ultra G1a, powered by the AMD Ryzen AI Max+ PRO 395 processor, the Revit Snowdon Tower sample project consumes 8.1 GB of GPU memory on load. This increases to 14.2 GB when rendering at 4K and 35.1 GB when rendering at 8K. With the test machine configured with 128 GB of system memory, of which 64 GB is allocated to the GPU, the system maintains ample headroom throughout. In contrast, when a discrete GPU runs out of dedicated memory, it must offload data to system memory — a process that can introduce latency, slow performance, and significantly increase render times.

utoCAD has enjoyed some major performance improvements over the last couple of releases, including file open times up to 11x faster and application launch speeds up to 4x faster1. One of the most impactful — yet less talked about — enhancements lies in the graphics pipeline. Users of AutoCAD 2026 and 2027 can expect noticeably smoother navigation of 3D models compared to AutoCAD 2025 and a significantly more responsive design experience when using ‘wireframe’, ‘conceptual’, and ‘hidden’ visual styles. In AutoCAD 2027 this extends to ‘realistic’, materials, and lighting. These improvements are enabled by enhancements to Graphics System Fabric (GSF), a next-generation graphics engine

built on Microsoft® DirectX® 12 that optimizes GPU utilization. GSF offloads more graphics processing from the CPU to the GPU and caches more data in GPU memory, allowing for faster access and better realtime performance. Another key advancement in AutoCAD is improved support for processors with integrated graphics. Historically, integrated GPUs often lacked sufficient

memory to be supported by GSF, which typically requires between 4 GB and 8 GB of dedicated GPU memory. That limitation is effectively removed with the AMD Ryzen AI Max PRO processor, which can allocate up to 96 GB (Ryzen AI Max+ PRO 395) or 160 GB (Ryzen AI Max+ PRO 495) of its total system memory to its integrated Radeon GPU — far exceeding the capacity of most discrete GPUs in its class. In real-world testing on an HP ZBook Ultra G1a powered by the AMD Ryzen AI Max+ PRO 395 processor, the benefits of GSF are clear. Navigating a complex Mastenbroek heavy machinery model (pictured) in hidden, conceptual or realistic view style is instant and fluid in AutoCAD 2027 with no loss of detail. In contrast, with AutoCAD 2025, there is a noticeable lag: mouse movements lead to delayed onscreen responses, as the software must first

lower model detail in order to maintain interactivity, before restoring full detail once mouse movement stops.

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Snowdon Tower project from Twinmotion

Sample architectural project from AutoCAD® Architecture

TWINMOTION: BEYOND BIM

To support the independent testing carried out by AEC Magazine for this special report, Autodesk provided complimentary licenses of Autodesk® AutoCAD® 2025/2027 and Autodesk® Revit® 2027 Footnote 1: Results may vary based on user experience, machine, operating system, filters, and even source material

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INTEGRATED GRAPHICS, REDEFINED Accelerated by the AMD Ryzen™ AI Max PRO processor, the HP ZBook Ultra G1a & G3a and the HP Z2 Mini G1a deliver impressive performance for CAD, viz and AI – without the need for a discrete GPU

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or CAD and BIM workflows, architects and engineers have traditionally relied on workstations with separate CPUs and discrete GPUs. Processors with integrated graphics have often fallen short — lacking the 3D performance, applicationspecific optimizations, and software certifications required for professional use. But the AMD Ryzen AI Max PRO processor at the heart of the HP ZBook Ultra G1a / G3a mobile workstations and HP Z2 Mini G1a desktop workstation is redefining what’s possible with integrated graphics. The AMD Ryzen AI Max+ PRO 395 and 495 processors feature powerful Radeon™ 8060S and 8065S GPUs respectively, delivering performance that rivals that of a mid-range discrete GPU. Both chips enable smooth, responsive viewports in Revit and AutoCAD, even when working with large models, and can also handle real-time visualization workflows in Twinmotion for

small to medium-sized projects. This leap in capability is powered not only by the AMD RDNA 3.5 graphics architecture, but also the ability to allocate large amounts of system memory to the GPU — far more than the fixed on-board memory of most comparable discrete GPUs. In the BIOS, users can allocate up to 96 GB with the AMD Ryzen AI Max+ PRO 395 and up to 160 GB with the AMD Ryzen AI Max+ PRO 495. However, dedicating large amounts of memory to the GPU isn’t always necessary. In some workflows the GPU can dynamically borrow additional memory from the system when needed, without the severe performance penalties that occur when discrete GPUs exceed their onboard memory and must fall back on slower system RAM. The processor’s large memory pool also unlocks new possibilities in AI. With 96 GB of allocated VRAM, the GPU can run

colossal 128B parameter Large Language Models (LLMs) – roughly the size of Chat GPT 3.0 – well beyond the limits of most fixed-memory GPUs. With 160 GB it can go even higher — up to 300B+ parameters at 4-bit quantization. For architects, more memory also unlocks practical creative advantages in other AIdriven workflows. Text-to-image tools like Stable Diffusion, ideal for early-stage viz, can benefit directly. With fast, direct access to a large pool of GPU memory, it becomes feasible to generate high-resolution images — far beyond the practical pixel limits imposed by GPUs with restricted memory. Finally, the AMD Ryzen AI Max+ PRO 395 and 495 also come with a Neural Processing Unit (NPU), which is designed to handle mainstream AI tasks efficiently. Respectively, the NPU can deliver up to 50 and 55 TOPS of AI performance, meeting Microsoft’s requirements for a Copilot+ PC.

Product features and availability may vary. For complete details & disclaimers, please visit www.hp.com/zbookultra

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he HP ZBook Ultra G1a (14-inch) and G3a (16-inch) (pictured right) are exceptionally powerful thin-andlight mobile workstations, powered by the AMD Ryzen AI Max PRO processor. Both deliver upgrades over comparable laptops: up to 16 high-performance CPU cores, stronger graphics (on the G1a), and generous system RAM — up to 128GB (G1a) or 192GB (G3a). They’re also remarkably slim: the G3a is just 17.9mm thick, the thinnest ZBook

ever, with the G1a close behind at 18.5mm. Starting weights are 1.50kg and 1.81kg respectively. HPs Vaporforce thermals keep both systems running cool and quiet. Efficient power delivery matters too: the G1a charges via a 100W or 140W adapter, while the G3a uses 140W or 180W. That higher 180W option lets the G3a’s processor draw more power, adding a performance edge on top of what it gets from the slightly more powerful Ryzen AI Max PRO 495 chip.

HP Z2 Mini G1a — mini desktop workstation

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espite its diminutive form factor, the HP Z2 Mini G1a is a very powerful desktop workstation. Like its laptop sibling, it features the same AMD Ryzen AI Max PRO processor. However, with a 300W internal power supply, the ultra compact desktop workstation can deliver significantly more sustained power to the processor, resulting in optimized performance in multi-threaded CPU and graphicsintensive workflows.

One of the most compelling use cases for the Z2 Mini G1a is in rack-mounted deployments, where multiple units serve as a centralized remote workstation resource. Using HP Z Remote Graphics Software (RGS), each user connects to their own dedicated workstation via a 1:1 session, ensuring both simplicity in deployment and predictable, high-performance access. Up to five workstations can be installed side by side in a 4U rack space, offering impressive density and scalability for teams.

Product features and availability may vary. For complete details & disclaimers, please visit www.hp.com/zbookultra

HP ZBook Ultra G1a & G3a — 14”/16” mobile workstation

AMD, the AMD Arrow logo, RDNA, Radeon, Ryzen, and combinations thereof are trademarks of Advanced Micro Devices, Inc.

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Cover story

Inside Motif

the agent-native BIM platform In the race to create the next generation of BIM, many firms have opted to develop in public, including Snaptrude, Qonic, Arcol, Hypar and others. Motif has taken a more old-school approach, opting for secrecy followed by a big reveal. In September we finally got to see what Amar Hanspal and his ex-Autodesk team have been building by Martyn Day

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or three years the question same software has concentrated their team must still create the full range of hanging over $46 million start- minds. Some are seeing increases of several building elements, specialist objects and up Motif, has been what, pre- hundred percent and are therefore explor- associated behaviours that Revit has accumulated over decades of development. cisely, Amar Hanspal and his ing how to shrink their Autodesk estate. Prior to the launch I had an exclusive Into that environment comes a product ex-Autodesk leadership team were building. The early releases, a collaborative with no traditional named-user seat two-and-a-half-hour pre-briefing with the whiteboard with model viewing and demands, no separate cloud collaboration team, a group of Autodesk alumni with redlining, invited the suspicion that this service required on top and no metered considerable Revit and AEC software was another companion product, a satel- AI access to simply view and navigate experience: Amar Hanspal (CEO and colite orbiting Revit rather than a replace- your own models. Whether Motif is good founder of Motif, and former co-CEO of ment for the mature BIM authoring tool. enough to replace Revit on production Autodesk), Brian Mathews (CTO and cofounder), Tatjana They also flew in the face Dzambazova (Director, of our February 2025 AI Solutions) and Matt interview with Hanspal, Jezyk (VP, Industry who agreed that taking The user interface, API and agents are clients of Solutions). This article on Revit was like climbthe same underlying data model, so the agent reflects what was actualing the North Face of the does not sit outside the BIM application trying ly in the box at the point Eiger, and that too many of launch. Motif will start-ups got lost in the to operate it through a layer of commands. It undoubtedly add feafoothills delivering side works against the project data itself? tures rapidly as it fleshes projects. out the product, but there That suspicion can is already enough there now be retired as Motif has launched its BIM authoring plat- projects remains an open question, but a to understand both what it is and where form. It’s browser-native, multi-thread- challenger is arriving just as some incum- the team thinks BIM is heading. ed, cloud native and built on the assump- bent customers are actively looking for a tion that AI agents are first-class users of way out, while also starting to question Yes, it is actually BIM the model, rather than a chat window or what comes next in BIM workflows. The first test for any Revit challenger is Motif is one platform, rather than a the boring one: is it actually BIM? Motif slow Model Context Protocol (MCP) boltstaged sequence of tools. Hanspal says the clears that hurdle. It is a browser-native ed onto the side. The timing matters. Many design IT modeller, documentation environment, BIM detail modeller running against a directors have told me that their historic conceptual tools and AI agents are all part cloud backend, with live plan and 3D Autodesk discounts are being removed at of the same application. What remains views side by side, dimensional feedback their next agreement, and the sizeable unfinished is object coverage, not the fun- while modelling and precise numeric increase they are facing for essentially the damental platform architecture: the Motif input rather than approximate sketching.

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1 BIM Revit import 2 Understanding BIM: data mining / data visualisation 3 Live plan and 3D views side by side 4 Agentic layout in 3D

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Parametric walls, doors, windows, floors, ceilings, levels and hosting behaviour are present. The initial use case is interior modelling and that is where our demonstration was mainly focused. Sheets are live views of the model rather than extracts. Change the plan and the sheet updates because both are reading the same underlying data. Every placed object has an identity within an ‘assets and instances’ hierarchy, making individual elements addressable by users, software and, more importantly, AI agents. When you use Motif, you are not necessarily working alone, but more on that later. Underneath is an accurate BRep geometry kernel, while imported meshes are accepted alongside it without forced conversion. Objects can hold multiple representations, including symbolic representations such as door swings in plan. Partial model loading operates on the client and server, while level of detail authoring exists at platform level, although it is not yet exposed as an enduser tool. Motif’s own description is that BIM is table stakes, and none of these capabilities is remarkable in isolation, but without them the rest of the proposition would not matter.

The database is the product

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The most important part of Motif is not the modelling interface, but the database underneath it. Motif describes the database as ‘schema-less’, although multischema is probably more accurate. Imported information can be mapped onto native Motif objects, retained in its original Revit or IFC schema, or represented in several schemas at once. Motif does not insist that everything entering the system must first be translated into one canonical object model, a significant departure from decades of attempts to create a universal building schema. The team recognised that AEC firms are going to use many tools and formats, and that a new platform should not try to own the customer by insisting everything be translated into its proprietary representation. The data model uses composition rather than deep inheritance hierarchies, drawSeptember / October 2026

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model, and supplies that context to the appropriate engine.

What an agent in the model can do

Motif supports IFC cation code and type system, ing partly on ideas from Pixar import and export avoiding a problem that and the games industry and, plagued extensible desktop according to the team, early conversations with Greg Schleusner (for- CAD systems. The AI story follows directly from this mer director of design technology at HOK and regular presenter at NXT BLD). There architecture. The user interface, API and is a dynamic-type registry, universal agents are clients of the same underlying identifiers and relationships between data model, so the agent does not sit outobjects. New types can be registered at side the BIM application trying to operate runtime, allowing developers to bring it through a layer of commands. It works their own objects and behaviours into a against the project data itself, an architecproject without waiting for Motif to add tural difference that is considerably more important than which LLM happens to be them to the core product. Motif is therefore not simply a model- connected to it. Motif is multi-model. It has its own ling tool for producing drawings. It is also intended to be a runtime environment in layer for making sense of objects and spawhich customers can craft their own tial relationships, then routes tasks to difdesign tools. Instead of waiting for the next ferent AI engines according to what is software release, practices can potentially required. These currently include Claude, create capabilities specific to their own Gemini and OpenAI models, among othworkflows, increasingly without needing ers, but the underlying engines are to understand conventional programming. expected to change over time. The durable Older AutoCAD users may remember part is Motif’s routing and spatial reasonthe ARX object-enabler problem. Receive ing layer rather than any particular LLM a drawing containing a custom object connected to it. Nor does Motif attempt to put an without the necessary software and you got proxy graphics rather than the real entire building into an LLM context thing. Motif is browser delivered, so window. It selects the data required for users are running against the same appli- the task, including spatial subsets of the

The demonstrations ranged from ordinary BIM interrogation to generating native parametric content. What caught my attention was not the number of things Motif could be prompted to do, but the fact that the output remained part of the BIM environment. One example was a compound BIM query on an imported model: find every door over two metres high without a fire rating located in a corridor. The agent also identified incorrectly categorised Revit objects, found missing fire data, calculated gross floor area and performed checks against requirements for a particular locality. It could then put its calculation method onto a sheet for checking, rather than simply returning an answer in a chat window. Language also becomes a navigation mechanism. The agent was asked to go to an auditorium, show a sightline from a particular row and display what somebody standing outside a glazed cafeteria would see. It could compare two design options, colour the differences according to rules and answer questions about what the project team had changed during the previous week. More interesting was the generation of editable BIM content. A marker sketch dropped onto the canvas became parametric walls, while a photograph became a parametric door rather than a mesh or rendered approximation. A bookshelf was generated from a short description, and a Gaudí-style door went through several rounds of correction without requiring the user to start again. Creating content in Motif looked, at times, like magic, although as we will come to later, there is a considerable difference between looking like magic and producing building information you can trust. Tatjana Dzambazova’s principle is that

Agent-native: why an MCP isn’t the same thing Every AEC software vendor is going to claim its BIM can connect to agents. The useful question is what those agents can reach. For an established desktop application, the route is normally through an API designed for external software developers. An MCP server can expose those API functions to an LLM, but it does not give the model deeper access to the application than the API beneath it permits.

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Matt Jezyk’s description of Revit is that the application acquired an API well after its original architecture was established, and MCP adds another layer above that API. I know of Revit users who have ended up writing their own Revit MCP instead of using the one Autodesk provided. It shows how quickly practices want agent access to existing BIM systems, but it does not change the architecture of Revit itself.

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Motif’s agent accesses the same underlying project model as the application interface and developer platform. Hanspal calls the direction a move from copilot towards autopilot. Put more simply, Motif’s agent is a native client of the BIM database rather than an external client operating through an API. That distinction is likely to matter more as almost every BIM vendor adds the

word ‘agent’ to its product literature. It also reflects a conviction about where projects live. In Motif’s reading, CAD records decisions made elsewhere, in conversations, email, specifications and building codes. Language is the material agents handle best, so an agent that can read both the model and the discussion around it occupies different territory from one that can only see geometry.

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Cover story users should not have to start from scratch. Existing drawings, models, photographs, text and previous work become inputs to the next operation. Image generators can already make convincing pictures of buildings, but Motif is attempting to make editable building objects that remain part of the model after the AI has finished, which is a considerably harder problem.

Skills, rather than scripts

nent meter: a task gets solved once, then every repetition sends another request to an expensive probabilistic model and consumes another batch of tokens. Motif’s argument is that repeated reasoning should become software. Use the model to solve a problem, generate deterministic code or a reusable skill, review the result, define its permissions and then run that capability repeatedly without paying an LLM to rediscover the same answer. Mathews calls this ‘not renting the same thought twice’, which is a useful way of thinking about where AI should sit in repeatable production workflows. If it works, every useful problem solved can become a permanent company capability rather than another AI transaction. BIM libraries traditionally contain objects, templates, scripts and standards, while an agent-native practice could also accumulate procedures: how it dimensions, checks, lays out, names, calculates, documents and responds to particular project conditions.

What we saw Motif do Interrogate: compound BIM queries; code and requirement checks; GFA calculations; identification of missing fire ratings and incorrectly categorised Revit objects; spatial navigation by language; designoption comparison; project history queries. Create: parametric walls from a marker

Motif has adopted the idea of skills from sketch; a parametric door from a photograph; a Corinthian column from an Anthropic’s Claude. Here, a skill is a reusimage; a sofa from an image; a parametric able instruction or capability described in bookshelf from a description; iterative ordinary language rather than convencreation of a complex door; object controls populated from manufacturer information; tional code. A skill could specify someparametric assets using 3D mechanical thing simple, such as how a practice CAD tools Solidworks and Onshape (the expects title blocks to be arranged, or FeatureScript capability). encode a more elaborate modelling or docDocument: automatic dimensions; umentation procedure. Hanspal compares sections and levels; schedules and tables; it with AutoLISP: a way for practices to specification information; Rhino geometry taken through to shop drawings with automate their own methods rather than element counts and lengths, without Revit. wait for the software developer to implement every workflow. Act outside the model: vendor research against selected objects and locations; Combine this with parametric object saving research into the project library; generation and some work currently sending options for approval; publishing done in family editors and visual pro- History, memory and failure renderings and information to Slack and Notion. Some external workflows remain gramming environments potentially Once several people and several agents one way at launch, with replies from Slack move elsewhere. If a useful door, furni- can work on a building model at the same and Notion not yet returning to Motif. ture object, checking routine or docu- time, keeping track of what happened mentation procedure can be created from becomes as important as concurrency language and examples, the barrier to itself. Motif’s history is effectively unlimcreating practice-specific automation in- ited and is not simply an undo stack. It also provides a safety net when agents get house falls considerably. records who did what, allows users to things wrong. Agents first produce a plan of what they intend to do, That does not automatiusers can inspect the result, cally turn everybody into a role-based access controls programmer by talking to a Motif has been designed on the assumption determine what an agent may machine. Practices are full of modify and previous states people who know how that agents will make mistakes, rather than be restored when somesomething should be done pretending sufficiently good prompting will can thing goes wrong. Motif has but struggle to describe the make the problem disappear been designed on the procedure so somebody else assumption that agents will can reproduce it. Turning make mistakes, rather than that tacit knowledge into explicit rules may prove harder than gen- return to an earlier state and provides a pretending sufficiently good prompting erating the code. record of the changes that produced the will make the problem disappear. Motif supports personal, project and current model. company-level skills, so a capability A previous state can also become the Getting existing work into Motif developed by one person can be promoted starting point for something new. Users No practice adopts a BIM platform it caninto a controlled company standard, with can select a point in the project history, not feed with existing work, and this is a management dashboard sitting above create a new model from it and develop it where some of Motif’s launch limitations them. The unresolved issue is arbitration, as a design option without disturbing the become important. Its approach to Revit as two skills may perform the same task original. With multiple people and agents and Rhino is to run connectors inside the differently for different building types, working concurrently, the history pro- source applications rather than rely offices, jurisdictions or clients. It is the old vides a way of making all that activity leg- entirely on standalone translators. The CAD standards problem applied to agent ible rather than ending up with a model plugins use the host applications to behaviour, and Motif does not yet have a that has changed without anyone being extract geometry and data, then stream complete answer. entirely sure how or why. subsequent changes as deltas rather than Dzambazova describes the objective as transferring the complete project again. Do not rent the same thought twice avoiding software that suffers a concusIFC import and export are supported, One of the more interesting ideas in the sion every time a project is opened, retain- while geometry and parameters can be briefing came from Brian Mathews and ing the changes and decisions that pro- modified in Motif and published back out. concerns the economics of AI. The obvious duced the current state rather than Jezyk says native Motif objects map cleanfailure mode for AI software is a perma- remembering only the latest model. This ly to IFC, Revit and Rhino equivalents.

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Cover story Full geometry write-back to Revit, however, is not part of the 8 September release. Comment round-tripping works and the mechanism for broader write-back exists, but Motif says customer workflows will determine how it is implemented. The bigger migration issue may be content libraries. A practice cannot currently press a button and migrate its Revit family library wholesale in the manner users of UNIFI, Pirros or Kinship might expect. Hanspal acknowledged the value immediately, and in our view this is one of the most significant gaps in the launch product. Years of Revit adoption have left practices with substantial investment embedded in families and standards, and abandoning that is a very different proposition from importing an RVT file.

Viewing is free outright. Querying depends on the query: asking the model to show all the windows is treated like viewing, while asking for the combined area of three objects involves computation and consumes tokens. AI credits are included with every active user and pooled across the organisation, with additional credits available to purchase. Multiple agents can run in a single session and by default an additional agent costs nothing extra, although each consumes tokens according to the task it is given. Motif is also offering a small group of early customers two active users for the price of one for their first three years. This is not general launch pricing, but an incentive for what Hanspal describes as

ison are capabilities of the common cloud project database rather than separate collaboration products. That gives Motif considerably more freedom over how those capabilities are priced. Autodesk could match elements of the model, but doing so would mean accepting some cannibalisation of products and licence structures that currently generate recurring revenue. A new entrant does not have that problem.

Where Motif starts

Motif is not pretending it can replace every Revit workflow on 8 September. Its first two target areas are interior fit-out and test fits, where it believes the available object coverage and packaged skills are already sufficient. It is also targeting firms What it costs looking for Rhino-todocumentation workMotif has rejected tradiThe requirement is no longer necessarily ten flows, allowing practices tional named-seat licensarchitects editing the same model to take Rhino geometry ing and, since our briefthrough to construction ing, has confirmed its simultaneously, but several agents checking, launch pricing. The model calculating, documenting or modifying different information without dropping into Revit simply as a pairs a platform fee for parts of it while people continue to work documentation engine. broad access with tokenThis is not the mainstream based pricing for AI, so BIM workflow, but the what a firm pays reflects how much the software is actually used the ‘true believers’ prepared to commit to firms interested in it include some leading, the platform at this early stage. For those influential, signature and boutique pracrather than simply who has a login. Standard platform pricing is $150 per firms it effectively halves the active-user tices. The roadmap for the remainder of active user per month, with unlimited cost. The comparison with Autodesk is the year includes the API and SDK, furprojects and unlimited user access, not simply about headline licence price, ther agent-building tools and continued including guest editors and reviewers. An although even there the arithmetic is expansion of object coverage and docuEnterprise tier starts at $200 per active uncomfortable, since a named annual mentation control over the following six user per month, adding compliance and Revit seat alone costs more per month months. security features, credit usage controls than Motif’s active-user fee before any colWho moves first? laboration service is added. and higher AI credit allowances. A Revit practice using Autodesk’s cloud The first adopters are unlikely to be averThe definition of ‘active’ is where the model departs from named-user subscrip- workflow combines authoring subscrip- age Revit practices. The obvious candidates tions. Active usage is set by a threshold of tions with BIM Collaborate Pro and other are technically mature design firms with meaningful editing by users or agents, Autodesk cloud services, so the cost of col- internal computational design groups. accounted for retrospectively in the man- laboration sits on top of the authoring They already have the people capable of ner of floating licensing, so occasional and environment. In Motif, collaboration, pro- exploiting a flexible data platform and, in light use creates no platform cost. ject history, rollback and version compar- many cases, Rhino-heavy workflows that

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The 1998 problem Revit’s original architecture dates from 1998, and many important model regeneration and editing operations remain heavily serial. Modern workstations therefore deliver less benefit than their high CPU core counts might suggest for many common Revit tasks. Mathews, once the VP of platform engineering at Autodesk, recalls moving an elevator shaft through a skyscraper model in Revit as a ninehour operation which subsequently

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failed on a conflict with a roof drain near the end of the process, requiring it to be restarted. He also remembers beginning a Los Angeles commute while waiting for a model to open and arriving before it had finished. Concurrency is another difference. Motif does not use Revit-style pessimistic locking for major model operations. Compatible changes can coexist, while conflicts are detected and presented for resolution. This matters particularly once agents

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become active participants in the project, allowing several processes to work against the model while people continue to use it. Motif’s graphics stack is its own, built on WebGPU with its own spatial index and font engine. The same graphics code can run in the browser, on an iPad or headlessly in the cloud when an agent needs geometric or visual information without a human display. Headless operation has been tested but will not be productised until

later in the year. Change propagation is multi-threaded, compute can be distributed and only the required parts of the project need to be loaded. Every demonstration video we saw was accelerated. Motif says it makes no compromise on speed and is faster than Revit, but no formal benchmark was provided during the briefing. The architecture makes better parallelism plausible, but we need to see it demonstrated on real projects before taking the performance claim as proven.

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fit one of Motif’s initial targets. Motif’s flexibility may initially work against it in the wider market. Many Revit users may have difficulty conceiving of an open platform on which they can invent their own process; they want a product with the process already decided. Skills could eventually bridge that gap, with practices, consultants and resellers packaging workflows so users inherit an established method rather than build one, but that ecosystem does not yet exist. Hanspal made one other admission worth repeating. Simultaneous multiuser modelling, presented as a headline feature of cloud BIM systems for more than a decade, is comparatively uncommon in practice, with most people simply needing to share what they are seeing. His example was a structural engineer pointing an iPhone at a monitor to show a colleague in Saudi Arabia what was happening in the model. That might appear to undermine Motif’s emphasis on concurrency, but agents change the equation. The requirement is no longer necessarily ten architects editing the same model simultaneously, but several agents checking, calculating, documenting or modifying different parts of it while people continue to work. Concurrency may turn out to be considerably more important for machine collaboration than it ever was for human collaboration.

How Autodesk might respond Autodesk’s obvious response might be that Revit and its wider platform will also have AI agents and MCP access. For many buyers that may be sufficient, particularly where changing authoring platform creates more disruption than the new capability saves. An MCP, however, does not remove the limitations of the API beneath it, and an API does not change the underlying data, geometry and concurrency architecture of the application it controls. Autodesk can respond, but it has to bring agents to software and commercial structures accumulated over decades while protecting existing revenue. Motif does not have those constraints and arrived late enough to design around many of the problems its predecessors discovered. Whether those architectural advantages outweigh Revit’s enormous installed workflow is the contest that now gets interesting.

The accuracy problem The demonstrations look impressive, but what happens when the agent is wrong? If a parametric door is generated from a photograph, what was the focal length of www.AECmag.com

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the lens, what has perspective done to its proportions, and which measurements are known rather than inferred? These are much less glamorous questions than image-to-BIM generation, but on a real project they matter rather more. The industry has already seen what happens when generation becomes a paid guessing loop. In our experience with Illoca Plamo, a new AEC-focused agentic 3D modelling workspace that turns sketches, images and prompts into editable parametric geometry, we encountered waits of around twenty-five minutes – for the software to generate a façade with twenty windows where the building had six bays. And it consumed roughly one thousand credits while correcting mistakes. Motif says its photo-derived assets are dimensionally accurate. The result is also a native parametric BIM object rather than a fixed image or mesh, so its properties can be edited directly to drive it to exact dimensions where required. That is an important distinction, although we have yet to test the dimensional accuracy ourselves on a real project. The Gaudí door demonstration showed correction as part of the workflow, while planning, inspection, project history and rollback are built into the product. The real test will come when an agent makes a mistake on a live project: whether the error is obvious, how much it costs to correct, whether the previous state can be restored cleanly, and whether the correction can be captured so the practice does not pay for the same mistake again. Motif is at least built for those questions, which makes it considerably more interesting than another AI assistant attached to an existing BIM application.

Conclusion Motif is the first Revit challenger I have seen where replacing Revit may not actually be the most interesting part. The company certainly wants Revit seats, but getting them will mean filling out its object coverage, proving performance on large projects, solving migration and convincing firms to move workflows carrying years of accumulated Revit content and knowledge. Even if Motif had every Revit capability tomorrow, sheer inertia would make that difficult. But Motif has not been designed simply to recreate Revit in a browser. The more interesting idea is a persistent project database in which people, agents, geometry, requirements, company skills, generated software, project history and external information can all work against the same underlying model. That is quite

What’s missing at launch Motif is not presenting the 8 September launch as the point at which every Revit workflow has been replicated. Among the gaps and unanswered questions from our briefing are full Revit geometry write-back, one-click migration of existing Revit family libraries, deeper Autodesk Construction Cloud integration, complete BIM object coverage, end-user Level of Detail (LoD) authoring, two-way communication through services such as Slack and Notion, and productised headless operation. Two claims also remain untested by AEC Magazine: Motif’s speed against Revit and its claim that photo-derived assets are dimensionally accurate, and we return to both below. Broader API, SDK and agent-building capabilities remain on the roadmap for the remainder of the year.

a different starting point from adding AI access to an existing BIM application through an API and MCP. There is plenty we still don’t know. We have not benchmarked Motif against Revit, used it on a large live project, migrated a substantial Revit estate or independently tested its claims for the dimensional accuracy of AI-generated building information. Object coverage remains incomplete and some important interoperability workflows are still coming, so anyone expecting a drop-in Revit replacement on 8 September is going to be disappointed. What Motif has demonstrated is a collection of capabilities that would be difficult to retrofit into some existing BIM systems. Its developers also have the advantage of knowing Revit, RVT, Autodesk and BIM customers extremely well, while having had the money and freedom to start again. The AI shift happened early enough in Motif’s development for the team to rethink parts of the architecture around agents rather than add them afterwards. Motif also arrives when leading firms are experimenting seriously with AI, building more tools internally and reconsidering their software stacks. Some of the practices most capable of exploiting it are already questioning both how much incumbent software they need and what they want the next generation of BIM tools to do. For years a new BIM application has effectively been judged by how closely it can reproduce what Revit already does. Motif still has a lot of catching up to do, but parts of it suggest that comparison may no longer be the most useful test. ■ www.motif.io

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Why AI should influence your next AEC workstation purchase When evaluating workstations, CPU and GPU horsepower for CAD, BIM and visualization are no longer the only benchmarks that matter. AEC firms should consider each machine’s AI capabilities as well.

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hen architecture, engineering and construction (AEC) firms evaluate new workstations, the questions are somewhat predictable. How well will they run Autodesk Revit®? Will they be able to handle large multi-disciplinary BIM models? Will they be fast enough for real-time visualization? Will they keep pace with increasingly complex multi-application workflows? Those questions still matter. But there’s one more worth adding to the list: how well will the workstations run AI? AI’s impact on AEC is already showing up, and things are moving unbelievably fast. But in an industry built on multi-year projects, nothing’s going to be replaced overnight. Instead, AI is quietly working its way into the tools firms already rely on for CAD, BIM, visualization and reality modeling, while entirely new AI-powered applications and workflows are emerging alongside them. For firms investing in workstations today, that raises an important point: the hardware they buy to support their AEC teams over the next three to five years may need to handle some significant AI workloads as well. AI is becoming part of the AEC workflow Most of the discussion around AI in AEC so far has been about the cloud. This includes frontier large language models (LLMs) like OpenAI’s ChatGPT and Anthropic’s Claude, image generators like Google’s Gemini (Nano Banana), and a growing list of AIpowered collaboration platforms and specialist AEC tools accessed through a web browser. Cloud AI will undoubtedly remain an important part of the AEC technology landscape. But more and more AI workloads can now run directly on the

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workstation, bringing new efficiencies to existing desktop workflows and helping uncover new insights from CAD, BIM and other project data. These include coding assistants, automation tools, image generators, and open-weight LLMs. In AEC, AI is progressively being used to automate repetitive BIM tasks, check models for code compliance, identify coordination issues, assist with scripting, generate visualizations, and even build agents. Running AI locally has real upside for AEC firms. Performance is more predictable, particularly when working with large datasets or iterative workflows. Sensitive intellectual property, client information and project data remain under tight control, and there may also be cost benefits overpaying for subscription or usage-based cloud AI services. But local AI also introduces a new set of demands on workstation hardware. AI changes the hardware equation Workstation specs have traditionally been defined by three things: CPU performance, GPU performance and system memory. That model still applies, but AI adds another dimension. A lot of AI models are memory hungry. Running a sophisticated local language or generative model can eat up tens of gigabytes of memory, depending on its size and precision. That can be a real problem for conventional workstation architectures, where CPU memory and GPU memory are two distinct pools. A professional GPU might offer exceptional graphics horsepower but still choke on a big AI model simply because its VRAM is capped. And it can be even worse on mobile workstations, where laptop GPUs typically have far less VRAM than their desktop equivalents.

This comes at a time when AEC workflows are already demanding. Few architects or engineers work in a single application all day. More likely, it’s Autodesk Revit or McNeel Rhino™ running alongside a real-time visualization tool such as Chaos Enscape™ or Epic Games Twinmotion™, a browser full of cloud services, collaboration platforms, and office tools. Throw reality modeling or computational design into the mix, and the workstation soon needs to juggle several applications at once. In short, AI just piles onto workflows that are already demanding — and that’s exactly where a new approach to workstation architecture could prove significant. A different workstation platform The AMD Ryzen™ AI Max PRO platform, found in machines like the new HP ZBook Ultra G3a mobile workstation and HP Z2 Mini G1a desktop workstation, takes a genuinely different approach. Instead of treating the CPU, GPU and AI accelerator as separate components, Ryzen™ AI Max PRO processors integrate them within a single system-on-chip (SoC), combining high-performance Zen 5 CPU cores, solid AMD Radeon™ integrated graphics and a neural processing unit (NPU), all sharing a single pool of high-bandwidth unified memory. That architecture can be particularly efficient for workloads that need to move data between CPU, GPU and AI accelerators. And, for AI specifically, memory capacity can matter as much as raw processing performance. The importance of unified memory Perhaps the most distinctive feature of the Ryzen ™ AI Max PRO platform is its flexible

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memory. The new HP ZBook Ultra G3a, for instance, can be configured with up to 192 GB of unified memory, but instead of having a fixed amount for graphics, the system can dish out memory dynamically based on what the workload actually needs – up to a whopping 160 GB specifically for the GPU. That could prove particularly relevant as local AI models become larger and more capable. A conventional discrete GPU might have plenty of processing performance, but not enough dedicated VRAM to even load the model, whether that’s simply querying a local LLM or developing a demanding autonomous agent. A unified memory pool gets around that problem by making substantially more memory available to graphics and AI workloads when needed. That same flexibility helps everyday AEC workflows as well. Large BIM models, complex geometry, high-resolution textures, visualization scenes and AI models are all fighting over system resources — and dynamically allocating memory is more flexible than permanently splitting it between system and GPU. That said, firms must be realistic about what Ryzen™ AI Max PRO processors can do. They should evaluate it against the applications their teams use, since different CAD, BIM and visualization workflows all have their own processor and graphics requirements. Discrete professional GPUs still matter a lot, especially for demanding visualization and certain reality modeling work. AMD Ryzen™ AI Max PRO processors can handle very large models, but it’s not going to out-muscle a high-end discrete GPU when it comes to raw processing power.

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Buy for today, but prepare for tomorrow No workstation can genuinely be considered future-proof — software is changing, AI is moving fast, and no one really knows what tools the industry will be using in five years. Still, it’s hard to argue against giving serious thought to future workloads when buying. When evaluating professional hardware AI readiness should now sit alongside CPU performance, GPU capability, memory capacity and application certification. That doesn’t mean AEC firms should abandon the old checklist — CAD, BIM and real-time visualization performance still matters. But it’s worth thinking ahead to what happens when AI needs to run alongside those applications, when teams begin searching large document collections or BIM models with local LLMs, or when AI-powered automation gets baked into desktop BIM workflows. The workstation is fast becoming the hub of a more complex computing environment. For practices investing in new hardware, workstations such as the HP ZBook Ultra G3a and HP Z2 Mini G1a make a compelling case. Built on AMD Ryzen™ AI Max PRO processors, they bring together strong CPU performance, capable integrated graphics, dedicated AI acceleration, and an unusually large, flexible unified memory pool in one tightly integrated package — designed to handle today’s AEC workflows with a fair bit of headroom for whatever AI throws at it next. So, the question isn’t just “How fast will this run my CAD, BIM and viz software?” anymore. It’s “What else will this workstation need to do over its lifetime?” AI is likely to be a big part of that answer.

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In AEC, AI is progressively being used to automate repetitive BIM tasks, check models for code compliance, identify coordination issues, assist with scripting, generate visualizations, and even build agents

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How local AI is transforming AEC workflows

AUTOM8LABS MCP Connector for Navisworks®

Open-weight AI models running on new generation workstations are giving AEC firms new ways to automate workflows while keeping data and costs under their control

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ost conversations around AI in AEC center on the cloud, and it’s easy to see why. The latest frontier models from OpenAI and Anthropic can interpret drawings, generate code and work with 3D geometry in ways that could only have been dreamed of just a few years ago. But AEC firms don’t have to send every AI workload to someone else’s server and pay to generate each token. A growing number of powerful open-weight models can run locally, delivering predictable costs, tight control over sensitive IP, and the option to fine-tune models on a firm’s own data. Making that practical, however, comes with its own challenges. Big AI models need big memory, and most

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workstations, laptops in particular, run out of GPU memory long before an architect or engineer runs out of ambition. A model that won’t fit in GPU memory will either fail to run or slow to a crawl, regardless of how fast the processor is. This is where the HP ZBook Ultra G3a mobile workstation and HP Z2 Mini G1a desktop workstation come into their own. Both are built around the AMD Ryzen™ AI Max PRO processor, which combines CPU, GPU and NPU with a large pool of unified memory. The ZBook Ultra G3a offers the larger capacity and can be configured with up to 192 GB, including as much as 160 GB available to the GPU. HP says that is enough to run models of up to 300 billion parameters locally, alongside BIM

authoring tools like Autodesk Revit®, all on a single machine. But what can AEC firms do with these powerful workstations? Talk to your CAD and BIM software Model Context Protocol (MCP) is emerging as one of the most significant technologies for firms that want to get more out of their existing desktop CAD and BIM tools. It is an open standard that lets an AI model connect to other software, with an MCP server translating the model’s requests into calls to the software’s Application Programming Interface (API). Instead of navigating menus and clicking buttons, users simply describe what they want in natural language.

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Autodesk has released several MCP servers for Revit 2027 as tech previews, initially read-only, followed by versions that can write to / modify the model. MCP servers also exist for a growing number of other AEC tools, from commercial providers such as AUTOM8LABS to open-source community projects, many built on pyRevit Typically, the MCP server runs on the workstation alongside the CAD or BIM software, while the LLM that interprets the user’s request runs in the cloud, on Claude or ChatGPT. But open-weight models such as Nemotron and Qwen can perform similar tasks locally, and the large, unified memory pool of AMD Ryzen™ AI Max PRO processors is now allowing firms to run bigger, or less aggressively quantized, models than would fit on a typical workstation GPU with fixed VRAM. For Revit, MCP has two main uses: querying Revit models and modifying them. Querying is the easiest place to start. Users could ask, “How many doors and windows are on Level 2?” or “List every room smaller than 9m².” The real power comes when writing back to the model. Depending on the capabilities of the MCP server, users could set up projects, generate grids and levels, edit parameters in bulk (e.g. “For all interior doors, set fire rating to 60 min”), annotate views, produce drawings and even create parametric Revit families. The technology is not yet sophisticated enough to generate a detailed model of an entire building, and all output should be manually checked, but given how fast the technology is evolving, that could change sooner rather than later. MCP-enabled AI agents could also be used to automate time-consuming Revit QA/QC workflows, such as comparing the model and documentation against the project execution plan, owner guidelines and firm standards. Lowering the barrier Beyond hardware, one of the major barriers to local AI has been knowledge and skills. Even installing the relevant AI models has been a challenge. HP’s new partnership with Perplexity promises to lower that barrier significantly. In Portable Computer mode, Perplexity Computer lets users easily download models such as Qwen or Nemotron and run inference entirely on the ZBook Ultra G3a. Agents can interrogate BIM models and other documents locally, including Revit models through the read-only version of Autodesk’s Revit 2027 MCP server, escalating to a frontier model like Claude or ChatGPT only if the user chooses. HP and Perplexity are also launching exclusive Perplexity ‘Skills’ for code compliance, design reviews and material specification research. There is no technical reason this must stop at Revit. With the right MCP server, the same approach could be extended to coordination tools, letting non-specialists manage coordination issues simply by asking questions or making requests. For example, with

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the AUTOM8LABS MCP Connector for Autodesk Navisworks®, users could simply tell the agent to “run the Structure vs MEP clash test and group the results by level” or “export the unresolved clashes from this test as a BCF package.”

Firms can connect local LLMs to Revit through MCP, use open‘Vibe coding’ your own tools AI also opens up significant opportunities for weight coding models to develop computer programming. So-called ‘vibe coding’, using AI to create scripts and applications by simply their own automation tools, and describing what you want, doesn’t have to be confined search years of specifications and to the cloud. Open-weight coding models such as Qwen3-Coder-Next can run locally on the AMD-based project data without anything HP Z workstations, so a firm’s proprietary standards, scripts and project data never leave the building. leaving their control

There are many possibilities. AI can write scripts for computational design tools like Grasshopper for Rhino™ and Dynamo, or produce pyRevit tools, Python scripts that run inside Revit and can be shared across teams as buttons and pulldowns. Firms can even build entire programs using opensource Software Development Kits (SDKs) from organizations such as That Open Company.

Documents, search and AMD software Beyond MCP and coding, local AI is also very well suited to general work using LLMs. Specifications, proposals, contracts, and company standards represent years of accumulated knowledge that can be difficult to find. A local LLM can summarize, compare and answer questions across those documents without anything leaving the firm’s control. Perplexity Computer can be configured to work with local files, but the AMD ecosystem offers additional options. AnythingLLM, an open-source app from Mintplex Labs that AMD supports on Ryzen™ AI hardware, is aimed at end users and provides a simple way to chat with local documents. Meanwhile, the AMD AI Playbooks provide stepby-step guides for building and deploying local AI applications. The Playbooks are aimed more at experienced software developers, but there are

also some for beginners, including one that generates images with ComfyUI and Z Image Turbo. Practical hardware strategies AEC firms have several ways to support these emerging AI capabilities. The simplest is to equip individual users with AI-capable workstations: the ZBook Ultra G3a for mobile professionals, or the HP Z2 Mini G1a for desk-based teams. Alternatively, one or more Z2 Mini G1a systems can act as a centrally managed, on-premise AI resource, delivering shared services across a practice or engineering office. Compact and stackable or easy to rack, with up to five units fitting in 4U, they also offer a practical way to centralize AI without the complexity of building dedicated server infrastructure. Looking ahead In AEC, local AI is not a single tool or feature. It is a growing set of technologies that can make familiar desktop workflows faster and more efficient. Firms can connect local LLMs to Revit through MCP, use open-weight coding models to develop their own automation tools, and search years of specifications and project data without anything leaving their control. That is where the HP ZBook Ultra G3a and HP Z2 Mini G1a fit in. As Windows-based workstations, they run Revit and the rest of the established AEC toolset natively, allowing AI capabilities to sit alongside existing workflows rather than in a separate environment. With the AMD Ryzen™ AI Max PRO processor’s large, unified memory, firms can explore all these approaches on a single machine without unpredictable cloud bills. As AI becomes as integral to professional workflows as CAD and BIM are today, the firms that experiment early will be best placed to take advantage of what comes next.

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BIM 2.0: unfinished business Motif and its BIM 2.0 rivals can’t yet do everything that Revit does. Neither could Revit when it first launched, and neither will whatever Autodesk builds to replace it. Martyn Day on why ‘come back when it’s finished’ is the wrong test.

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Revit has had more than a quarter of a or years, architects have com- have built up around it. That is the reaplained about Revit. It is slow, son architects often give for not making century to accumulate all this. Nobody expensive, awkward to auto- the move. More accurately, it is the rea- starting today with tens of millions of dollars in venture capital funding is mate and built on computer sci- son moving is hard. Consider what ‘finished’ actually going to reproduce it at launch. What ence conceived in the late 1990s. Practices buy workstations with 16, 32 or involves. A BIM authoring system needs a matters is what these new applications 64 cores, then spend much of the work- geometry kernel and graphics engine can already replace and whether the ing day inside an application that makes capable of handling very large buildings architecture underneath can take them limited use of them. Now there is a new at interactive speeds, parametrics, con- somewhere Revit cannot easily go. The sceptics do have one good argucomplaint, highlighted by the launch of straints and change propagation. Objects Motif this week: the alternatives aren’t need multiple representations, appearing ment. A tool that cannot produce a co-ordinated construction yet finished. Of course document set cannot they aren’t. Nothing deliver a building, and that sets out to chalPutting an agent on old computer science does not documentation is lenge 26 years of software development is make the underlying computer science new. It makes exactly where most going to be finished at the old computer science the bottleneck for the agent BIM 2.0 tools are thinnest. But plenty of launch, and nothing architectural work ever will be, if the does not end in a tenentry requirement is doing everything that Revit already does. differently in 3D, plan, section, schedules der set. Competitions, feasibility, concept Motif joins a growing group of what and quantities. Then there are families, and early-stage design, interior fit-outs this magazine has called BIM 2.0 appli- types, materials, annotation, dimensions, and option studies all offer places to start. cations: Snaptrude, Qonic, Arcol, Hypar tagging, sheets, revisions, title blocks, vis- The useful test is ‘can it do this workflow?’, and others, each rethinking BIM around ualisation, phasing, work sharing, version not whether it can swallow Revit whole. cloud computing, modern data architec- history, IFC / DWG, APIs, scripting, tures and, latterly, AI. None has Revit’s stairs, railings, curtain walls, roofs, struc- Revit’s greatest strength isn’t Revit breadth and none can replicate every tures and MEP. Then there are edge cases. Revit’s greatest strength is the ecosystem family, plug-in, Dynamo script, template, Add cloud infrastructure, security, per- customers have built around it: RVT standard and workaround that practices missions and now an AI stack. archives, family libraries, scripts, plug-

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ins, training, office standards, internal expertise and hundreds of undocumented fixes that everyone knows because that is simply how the job gets done. Autodesk did not create all of this. Practices have spent 15 to 20 years building much of it themselves, at considerable expense, and that investment is now one of the biggest obstacles to moving elsewhere. Some of it is genuine intellectual property and hard-won expertise, but some of it is scar tissue. A surprising amount of what we now regard as BIM expertise has grown out of learning how to keep increasingly complicated Revit projects running. Large Revit projects are routinely divided into separate models by building, level, zone or discipline to keep them manageable. Some of that division is legitimate: disciplines own their models, contracts draw boundaries, and federating models by author is how openBIM is meant to work. But a great deal of additional fragmentation is driven by performance, with projects split and linked, separated into worksets and selectively loaded to keep them responsive. These are no longer ingenious workarounds invented by desperate BIM managers; they are established Revit practice, and Autodesk www.AECmag.com

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itself documents them as ways of improving performance on large projects. The RVT format also changes between releases and newer files cannot simply be opened by the previous version. Consultants therefore need to coordinate which release is being used, upgrading a live project is approached cautiously, and practices commonly keep multiple versions installed to support existing jobs and libraries. Autodesk itself recommends planning upgrades around suitable project milestones. A continuously versioned cloud model has its own problems, but it does not have quite this annual file-format ritual. Cloud collaboration adds another layer. Revit Cloud Worksharing is not included with a Revit subscription, or with the AEC Collection. Autodesk requires each person authoring a cloudworkshared Revit model to have the additional Forma Design Collaboration entitlement, previously called BIM Collaborate Pro. Customers who want Autodesk’s cloud collaboration environment therefore pay for another service on top of the authoring application. Autodesk’s own cloud-worksharing guidance warns that opening, synchro-

nising and saving can become problematic as file sizes increase, and recommends techniques such as opening only necessary worksets, reducing loaded RVT links and avoiding simultaneous synchronisations. None of this makes Revit unusable. Millions of people successfully deliver buildings with it every day. It is simply part of the accumulated operational knowledge required to make large Revit projects work. We have become extremely good at making Revit work, and after two decades it can be difficult to distinguish expertise in designing buildings from expertise in coping with the software used to design them. A new platform that removes some of those workarounds isn’t necessarily asking a practice to throw away valuable knowledge. It may be asking why some of that knowledge needs to exist in the first place.

We have been here before None of the frustration with Revit is new. In July 2020, 17 UK practices, among them Zaha Hadid Architects, Grimshaw, Rogers Stirk Harbour + Partners and Wilkinson Eyre, wrote publicly to Andrew Anagnost with the results of September / October 2026

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their own survey: cost of ownership up by mental architecture was conceived for accounts, I would expect Autodesk to as much as 70 per cent between 2015 and another era of computing. Revit dates from accelerate the creation tools in Forma. 2019, five different licence models in as a world of desktop CPUs, local files and Autodesk already has Forma Building many years, and a platform that had gone limited GPU compute. Autodesk can add Design and has said that more workflows from industry enabler to ‘constraint and cloud services, APIs and AI around it, and currently supported in Revit will move bottleneck’, with practices paying more clearly is doing so. But an AI agent ulti- there over time. A few thousand serious and using Revit less. Within weeks the mately must work at the speed of the appli- customers putting real projects through cation underneath it, and within its con- competing tools would send a considerasignatories and supporters passed 100. Six years later, Autodesk’s public Revit straints. Putting an agent on old computer bly stronger signal than another decade roadmap still contains some remarkably science does not make the underlying com- of requests on the Revit Ideas forum. The fundamental requests. Stairs and rail- puter science new. It makes the old com- challengers do not have to replace Revit to have an effect; they only have to ings, curtain walls and materials remain puter science the bottleneck for the agent. Autodesk’s messaging about what become credible enough to make recurring areas of user frustration, many workflows remain heavily dependent on happens next has been inconsistent. At Autodesk respond. At some point Autodesk has the same single-core performance, large models NXT BLD in 2025, Autodesk’s Carl still need careful management. To Christensen was asked directly whether migration problem as every BIM 2.0 Autodesk’s credit, Revit 2027 finally Forma was going to replace Revit or developer. Things will be missing, workshipped an improved graphics engine, AutoCAD. His answer was no, subse- flows will have to change, and accumubut the headline additions were quently explaining the relationship as lated knowledge will not transfer neatly, Autodesk Assistant, MCP access and the ‘AND, not OR’. At NXT BLD 2026 there although Autodesk has the considerable Forma Connected Client. The wrapping was a rather different message from advantage of owning both ends of the is arriving faster than the contents are Christensen, with Forma described as journey and has already started moving the direction in which Revit ultimately parts of its customers’ workflow onto the being fixed. Autodesk has made this transition moves. Then, in a subsequent Autodesk new architecture. That is why I find the completeness before. When it bought Revit in 2002, interview with Christensen and Dan argument against two years after Autodesk’s competlaunch, its architecitors increasingly tural strategy was New BIM applications do not mature in a development strange. Motif, Architectural Desktop (ADT), an lab. They mature on awkward projects, under impossible Qonic or Snaptrude are dismissed object layer on standards, with difficult clients and experienced users because they canAutoCAD with an who know where the bodies are buried not yet reproduce installed base, conthe whole Revit tent ecosystem and workflow, while years of customer investment behind it. Revit could not do Lohmeyer, the wording shifted again. Autodesk is progressively moving pieces most of what ADT users did every day, Thinking of Forma as a replacement for of that workflow onto a platform which but Autodesk did not wait for parity. It Revit was described as ‘the wrong fram- cannot reproduce the whole Revit workgave Revit to ADT subscription custom- ing’, while Autodesk simultaneously flow either. I doubt Autodesk’s customers at no charge, put an unfinished prod- explained that, over time, many work- ers will be asked to wait until every last uct on thousands of desks and let firms flows supported by Revit today will be part of Forma is finished before they are encouraged to use it. The transition has discover whether the new architecture enabled in Forma. was worth the missing features. Maybe ‘replacement’ is the wrong already started. It still took the best part of a decade for word. Look at what Autodesk is actually Revit to overtake ADT, which was building. Revit is now a Forma Connected The price of staying put renamed AutoCAD Architecture and Client, Revit cloud data is being exposed From conversations I have had with gradually faded from importance. through the AEC Data Model and its large Autodesk customers, the cost of Autodesk wanted customers to try the APIs, Autodesk Construction Cloud has remaining within the existing ecosystem new architecture before it had caught up become part of Forma, and collaboration, is becoming an increasingly important with the old one. co-ordination, data and increasingly AI issue. Some firms tell me that historic Motif is attempting something similar are being assembled around the new discounts are disappearing as agreefrom outside the Autodesk ecosystem. Its cloud platform. ments are renegotiated, producing suboffer to a small group of early customers Autodesk says Revit will continue to be stantial increases in what they are being of two active users for the price of one for developed alongside it, and I have no rea- asked to pay. three years is obviously not the same son to doubt that. But as more of the Those same firms are extraordinarily deal, but the logic is familiar: reduce the workflows, data and services that make difficult for Autodesk’s competitors to cost and risk of trying the new architec- up BIM move into Forma, the argument penetrate because so much of their proture while there are still holes in it. over whether Forma technically ‘replac- duction infrastructure is tied to Revit. It es’ Revit starts to become semantic. is a wonderful position for any incumAutodesk has the same problem If Motif, Snaptrude, Qonic, Arcol or the bent software company to occupy. A cusThe question I keep coming back to is how other BIM 2.0 software developers start tomer with no credible alternative has far you can take software whose funda- gaining meaningful traction inside Revit very little negotiating leverage, whatever

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the reasoning behind individual Autodesk pricing decisions. Move even one genuine workflow away from Revit and the conversation changes. The firm that has learnt something about the alternatives, discovered what does and doesn’t work and, most importantly, demonstrated that moving is possible, walks into its next renewal able to say so, and Autodesk’s account managers know the difference.

privacy and sensitive project data matter. Motif has thought about this. CTO Brian Mathews told me that although Motif is a cloud application today, it has been architected so that it could run on the desktop. He expects some compute to come back onto local machines as AI becomes a larger part of design. Shared data and collaboration may live in the cloud while geometry, AI and agent compute increasingly happen locally.

Swapping one dependence for another

Conclusion

BIM 2.0 brings its own risks. Cloudnative BIM can create a more complete dependence on a vendor than a proprietary file format does. An RVT file can sit on a server you control, long after a subscription ends. You may no longer have a licensed copy of Revit with which to edit it, but the file remains in your possession and can still be inspected using Autodesk’s viewer. With a cloud-native model, continued access depends much more directly on the vendor’s service, commercial terms and export mechanisms. That matters when the vendor is a startup which may change its pricing, be acquired or fail. Export fidelity to IFC needs testing at scale, offline working cannot be assumed, and getting out may depend on whatever exports and APIs the vendor provides rather than simply taking possession of a file on disk. So test the exits as hard as the modelling. Ask what comes out, in what format, at what fidelity and what it costs to leave. A tool that removes Revit’s workarounds but replaces a file you possess with a cloud subscription from which you cannot easily escape has not removed the toll. It has moved the tollbooth. Cloud-native also does not have to mean that every calculation stays in the cloud forever. AI may make powerful local hardware much more relevant again, particularly where cost, latency,

Practices need to give BIM 2.0 a chance. Nobody is owed your business simply for turning up, and nobody should rip out Revit next Monday. There is a large distance between doing nothing and betting the practice on somebody else’s Series A. Buy a few licences, put real people on the software and find a competition, an interior fit-out, an early-stage design project or another workflow where failure isn’t going to bring the practice to its knees. Give the BIM 2.0 developers your difficult models and horrible families. Tell them what is missing, break their software and make them fix the things that stop you using it. New BIM applications do not mature in a development lab. They mature on awkward projects, under impossible standards, with difficult clients and experienced users who know where the bodies are buried. If the software falls over, you have learned something and so have its developers. If nobody serious tries it because it cannot yet do everything Revit does, firms will keep waiting for mature alternatives while the alternatives wait for the real projects needed to mature. There is a clock running. Most BIM 2.0 companies are funded by venture capital, while others, such as Qonic, have private backing, but none have unlimited time or money. Investors will eventually want evidence of a market beyond demos, conference applause and enthusiastic early

adopters. If customers remain interested in principle but unwilling to put real projects through the software, some of these companies will run out of road before they have had time to build what the industry says it wants. That would be a particularly stupid outcome for an industry that is extremely good at complaining about the software it has standardised on, but remarkably slow to engage with people trying to write something different. Real engagement gives developers the information they need to improve the software and the traction they need to fund the next stage. If AEC wants better choices, it has to help create them. What I want to see is several serious software vendors competing for AEC customers again, forced to improve their tools, respond to users and keep moving the technology forward. Competition is good. Some of today’s challengers will disappear, but even forcing the incumbents to compete harder would be progress. Motif is incomplete and so are its competitors. Some will fail, some may simply turn out not to be very good, and the best may be bought by one of the incumbents, which is how Autodesk acquired Revit in the first place. But there is a limited window in which customers can help turn this burst of BIM development and hundreds of millions of dollars of investment into a genuinely more competitive market. If the industry’s response is still ‘come back when you can replace everything we do in Revit’, most of these opportunities will be gone by the time we decide we wanted them, and what remains will be more of the same, at whatever Autodesk decides it costs. So, get involved while they are still here. And maybe send this article to the board the next time you have to explain why the demands of your current BIM provider are about to blow through the IT budget.

Round up: the BIM 2.0 pioneers Arcol

■ www.arcol.io

Hypar

■ www.hypar.io

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Motif

■ www.motif.io

Qonic

■ www.qonic.com

Snaptrude

■ www.snaptrude.com

NeoBIM

■ neoone.neobim.ai

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Technology

Autodesk’s most ambitious AI play yet At Autodesk University Autodesk presented an AI ambition that goes beyond faster tools: a connected Assistant that decides how to solve tasks on your behalf. But as Greg Corke found on the Expo floor, there’s also a growing appetite for local AI, keeping data and costs firmly under control

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utodesk no longer just wants to sell you better software; it wants to do a lot of your work for you. At Autodesk University 2026, that ambition was presented as ‘Project Intelligence’, described by Autodesk CEO Andrew Anagnost during his keynote as a “connected brain at the centre that brings every data point, every decision, every discipline together – a brain that grows smarter as a project advances.” Naturally, AI dominated the agenda. Almost every major announcement came wrapped in talk of agents, models and context. What set this AU apart from the previous two, however, was a shift from talk of time-saving features in individual CAD and BIM tools to a more ambitious AI proposition: an intelligent AI intended to turn a user’s stated intent into work they can review, refine, and use, without them having to navigate products, workflows, data and complex UIs. In other words, users state what they want, and Autodesk becomes the orchestrator calling on the relevant tools from its broad software ecosystem.

Autodesk Assistant goes agent-first The headline announcement was that Autodesk Assistant is going solo. Anagnost explained that it will be available as a “standalone agent-first experience, one that reaches across products.” Mike Haley, SVP research gave the rationale in the pre-event press briefing stating that it is “for work that doesn’t fit neatly inside a product like Revit or AutoCAD or Fusion.” The software is still in development and expected to launch next year. Standalone Assistant is not a replacement for the Assistants already found 40

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inside Revit, AutoCAD, Fusion, Civil 3D, Forma Data Management, Autodesk Build and others. In fact, Amy Bunszel, executive VP, AEC solutions, confirmed that Autodesk will keep expanding the in-product versions. It does not feel like the name matches the ambition. Autodesk Assistant sounds like a help pane, which is mostly what it was until it got its first AI injection last year. What was shown at AU 2026 is closer to an AI-powered operating environment that can spawn agents, reach across products, disciplines, datasets and organisations, and even generate its own user interface. That sounds less like an assistant and more like the beating heart of a platform — in Autodesk’s case, its industry clouds: Forma, Fusion and Flow.

Who is standalone Assistant for? The core users of the Standalone Assistant, according to Bunszel, will be those following what Autodesk internally calls critical user journeys, “Things our customers are trying to do across products and across projects,” she said. These users will likely span both ends of the skill curve, from experienced operators to laypeople. “I could imagine in the future, a customer who wants to use Revit will just use Assistant in Revit,” she said. “A customer who wants to do Forma Site Design, they can

go directly there. But someone who is trying to take maybe a higher order view on a company’s past projects or how maybe the civil on this project is connected to the water over here, they’ll be able to do some of those things through the Standalone Assistant.” From a technology standpoint it’s an access layer over a mesh of components “Some of it is MCPs [Model Context Protocol], some of it is agents that we’re building, agents customers are bringing, cloud services we have on APS,” said Bunszel.

Project-aware, not prompt-driven Over the past few weeks, it’s been hard to ignore the remarkable 3D-modelling experiments people are sharing with OpenAI’s GPT-6 Astra, driving tools like Blender, Rhino and Revit (see page 18). Autodesk’s argument against the frontier labs is that generic chat is the wrong approach for design and that AI models powering Autodesk’s neural CAD directly reason in geometry, rather than speaking to an API about geometry. “You can’t just rely on the large language models to solve problems,” said Haley. “You’ve got to go much deeper,” because the context of design work is “not just a pile of text. It’s geometry. It’s engineering intent. It’s the project history. It’s the relationships behind the work.”

Autodesk CTO Raji Arasu

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Autodesk demonstrated how Standalone Assistant might operate in the future

That capability now has a name: ty platform and recommended changes to Small is the point Autodesk Context, introduced on stage reduce the structure’s carbon footprint. Anagnost expanded on the Autodesk vs by CTO Raji Arasu as something “not Through another MCP it then tapped into frontier model argument in his press pulled from a prompt or a siloed data Artelia’s project data to assess the cost Q&A. Large horizontal models “trained source” but built from the thousands of impact, drawing on historical material and on billions and billions of parameters are decisions a company makes. Critically, installation costs from previous projects. a very expensive way to solve certain “NTI knows carbon, Artelia knows its specific problems, especially when the she said, “It’s not just about tracking own project history, and Autodesk knows specific problem has to be described in a what happened. It is why it happened.” She described a familiar problem: six the model,” said Arasu. “What [usually] highly constrained way. The models just months after the project finishes, nobody took weeks across multiple systems just burn through tokens like you can’t can say why the wall moved. “The sys- happened in one connected workflow.” believe,” he said. Another key layer in the stack is tem may know what has changed, but Autodesk’s own models “used to be why was that decision made? And too Autodesk AI Orchestrator, which as Arasu called large,” he said. “They’re called often that context is gone, locked in explained, is based on the premise that no medium to small now.” single AI model is best for every job. someone’s head.” The orchestration layer in Autodesk It sits beneath Assistant and, according Assistant is designed to take advantage Context also captures the ‘when’. It’s not just a timestamp said of that. “These are very Arasu, but “the state of the cheap to execute, you workflow at the moment know, pennies relative to that decision was made — dollars,” he said. Autodesk Assistant is shaping up to be the the constraints, the data Small is also better for thing that decides, on your behalf, how to points, the people that led speed and accuracy, he solve a task — not just a faster way to use to making that decision. added. “Because it’s “It’s a map of how informatrained for a narrower Revit, Fusion or Civil 3D tion, people, decisions, and problem, it generates at a activities in the project relate higher level of accuracy to one another,” she said. probabilistically.” Arasu acknowledged that projects to Haley, determines “which AI model is Anagnost also explained that don’t live in a single system, so Autodesk best suited to the task based on accuracy, Autodesk is bringing in an open-weight has launched Assistant Builder, which speed, security, and cost.” It constantly model because it found the performance extends the reach of Autodesk’s AI by tests Autodesk’s own models, such as neu- is just as good at certain problems as the letting organisations connect their own ral CAD, alongside frontier models, and best model coming from Claude. AI systems (and those of their partners) routes work accordingly, “sometimes one Towards a single model model, sometimes several,” said Arasu. to Autodesk Assistant. Design work “is not just words. It’s Neural CAD is Autodesk’s in-house AI To demonstrate the capabilities of Assistant Builder, Autodesk used a geometry, schedules, materials, simula- design technology, designed to reason worked example from Artelia, one of tion,” said Arasu, needing specialised directly about 3D geometry and physics. Denmark’s largest engineering firms: a models “optimised for speed, precision, It’s not a single model, as Tonya Custis, college dorm project showing how a cost, and deterministic understanding.” senior director of AI research, explained, “[Customers] should not have to evalu- “Neural CAD is just the term that we use structural engineer could optimise the design for both sustainability and cost ate every model or benchmark every work- to talk about all of our generative models flow,” she said. “That is our job.” The pool — primarily for geometry, design and without leaving Revit. Through an MCP server, Autodesk currently spans Autodesk-built models, manufacturing, and AEC.” Assistant connected to NTI’s sustainabili- Claude, OpenAI and open-weight models. Individual models are already being

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door bigger’ or ‘add some used in Fusion, Revit, and Assistant Builder extends Forma, typically as “one neu- the reach of Autodesk’s AI by solar panels here,’ before the letting organisations connect design becomes a 3D object. ral CAD model surrounded their own AI systems (and Crucially it’s not a one-shot by other models.” The longthose of their partners) to Autodesk Assistant system - the user could iterer-term goal is consolidation: ate on it dozens of times. “Eventually we’re working Autodesk is also exploring ways to on how do we train one big model for all those things,” she said. “When you build make existing tools easier to use and, in a model that learns all the tasks at once, the future, Assistant may be able to generate UIs on the fly. “If you wanted a user it’s better at all the tasks.” interface to control Revit to make someVoice, sketch, and a simplified Revit thing very easy to do, you can ask for it, it’ll generate that,” said Autodesk is exploring new ways to inter- and act with its products. Its R&D team Michelangelo Capraro, distinguished showed Project Quill, combining sketch- experience architect at Autodesk. A simplified Revit front end has been ing, speech and AI: “Some things are very easy to sketch but hard to describe; on architects’ wish lists for years. AI may some things are easy to describe but hard offer a route to that, suggesting there to sketch,” said an Autodesk spokesper- could be a shift from fixed software interson. “Combining those together lets peo- faces to ones that are task specific. ple get to what they’re trying to express The standalone Assistant demo more quickly and easily.” Users start from a rough napkin sketch, On stage Autodesk demonstrated how then circle something and say ‘make the Standalone Assistant might operate in

the future. It opened with a project manager “already behind” on emails, RFIs and issues, before Assistant surfaced the critical items across multiple projects “intelligently assembled by agents.” On one residential project, a client requested more rentable area by moving a wall three feet. Rather than type, the project manager asked: “How will this change impact the schedule, and what are the downstream impacts?” Assistant responded with a Gantt chart that identified schedule hotspots. The architect then asked it to “generate an LoD 300 apartment with walls and doors.” Assistant invoked an “industry foundation model trained specifically to understand and generate building geometry” using Building Layout Explorer to stream a 3D model in real time. It then fitted room layouts into every different shaped and sized unit across the building. This was presented as “minutes of work” instead of a day. Next came an “agentic workflow” in Assistant Builder: “Run the slatted facade workflow using dimensions from this attached image.” Assistant extracted dimensions from a sketch and generated some metal brackets for the wooden slats. The workflow then crossed products and industries. A design engineer in Fusion received the specification, Assistant found a similar bracket in the archive, updated it automatically, generated more than a thousand fascia options from images and then packaged everything back to the architect. Anagnost’s summary got to the heart of Autodesk’s vision for Assistant: people should focus on the outcome, the problems they are trying to solve, rather

The current Forma to Revit workflow As Forma moves further into Revit territory, Autodesk showed a typical Forma to Revit workflow, from earlystage site analysis through detailed building design and collaboration.

wind, microclimate and noise, to clarify a site’s opportunities and constraints before commitments are made. Multiple analyses can be evaluated alongside each other, balancing trade-offs.

architects away from thinking in building elements — necessary abstractions — and toward thinking in terms of use, as he put it: “now I want to design apartments.”

Forma Site Design

Forma Building Design

Into Revit

Once the massing is settled, Building Design is used to develop the proposal with greater precision and detail, including exploring facades and floor plan layouts. The philosophy is unitbased and takes a fundamentally different approach to Revit. Arne Bjelland, principal product manager at Forma Building Design, explained that the goal is to move

Through a connected workflow, users can take their project from Forma into Revit for detailed design: simply pick a project folder, save straight to Forma Data Management and open it from Revit as a cloud model. The geometry arrives as native Revit elements walls, levels, components. As Forma’s capabilities evolve, Autodesk explains you can get a

Everything starts with geolocation — now the same geolocation as Revit and Civil 3D — which pulls in satellite imagery, terrain, roads and context buildings to help an architect quickly figure out how many square feet of residential, commercial, etc. they can get from a specific site. Early massing options can then be explored against sunlight, visibility,

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much higher level of detail in Forma before you fully commit to Revit.

Revit: Forma Connected Client The Connected Client workflow connects Revit and Site Design models to a “shared scenario” and brings shared tools — contextual data, wind analysis, microclimate — directly inside Revit, carrying Forma analyses into the Revit experience and preserving design context as the project progresses. Revit geometry can go back to Forma, but as a mesh for analysis rather than editable Forma geometry. That’s because in Revit you can model at a much higher level of detail.

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Technology than searching for the tools and capabilities needed to solve them.

The transparency challenge Every firm that has experimented with frontier models knows the feeling: an impressive result, and no idea what it cost. To give customers more predictability, Anagnost says the plan is to amortise, “Some customers will overpay for something simple and underpay for something complex, and the average will work out to something that we can deploy at a reasonable margin,” he said in his press Q&A. Of course, just because something is predictable does not mean it’s visibile. Users of Adobe Creative Cloud, for example, know immediately how much a generative image will cost. Put to Bunszel if Autodesk might adopt a similar model, her answer was that it’s “very realistic”. Pushed on Autodesk giving customers reports that are granular enough to show who spent tokens and where, she said, “For the expensive features, there’ll probably be a rate table, so that people will know what happens when I push the button on these things.” Routine AI, she said, should be included in subscription, with charges reserved for things “highly valuable, expensive to run.” Transparency of another kind is being formalised more. Autodesk’s trusted AI team has updated its AI transparency cards, which it likens to product nutrition labels, and is surfacing them inside Assistant itself. According to Sebastian Goodwin, chief trust officer, training uses only “the undifferentiated stuff that is common across the data set to automate monotonous tasks. Labelling things in drawings, things like that, are not anyone’s secret sauce.”

Forma Board Board is a shared, BIM-connected review space covering a wide range of design files, which Autodesk describes as an infinite canvas linked to a project and available across the lifecycle. Along with PDFs and images, project teams can pull in dynamic BIM data, such as Revit sheets and views, enabling architects, engineers, clients, and other stakeholders to add markups / annotation to current project information rather than static exports. It also supports AI renderings so users can turn images, PDFs, and Revit views into rich visuals. The software is genuinely multiplayer, but for video conferencing it must run alongside Teams or Zoom. Commercially, it comes with Forma Data Management and Data Management Essentials,

Of course, it’s Autodesk, not the cus- is defined by reversibility, says Bunszel, tomer, that decides what is “secret sauce” “You can undo it… you can get back to and what is not. where you were.” Meanwhile, an experiAdvanced features can be switched off, mental version, which can write without with the caveat that “if you opt out, you asking the user to confirm each action, can’t use the feature,” said Goodwin. “could be riskier,” she says. “But we still Anagnost’s example of this was the new wanted to put it out there, knowing Layout Explorer in Forma there’s third-party ones because it has the potential doing this anyway.” “to maybe leak somebody’s In the AU Expo, Symetri In the future, proprietary IP into how was promoting an MCP floor plan layouts are done.” Assistant may server that exposes around 30 read-write tools in be able to Beyond Assistant Naviate, a productivity generate UIs plug-in for Revit and other Through Model Context on the fly. “If Autodesk tools. pyRevitProtocol (MCP), an emergbased MCPs offer another ing standard that lets exteryou wanted a modelling route. And as nal large language models user interface Nvidia’s Sean Young (LLMs) interact with a to control pointed out in his AU presrange of software, the Revit to make entation, there’s also the plumbing into Autodesk DIY route via AI coding, products is open. something “As long as there’s an API, Autodesk’s official Revit very easy to you can write your own,” MCP server was ‘read-only’, do, you can he said. And he has done it although that changed just repeatedly, “I’ve made before AU with the release ask for it, and MCP servers for a dozen of a ‘write’ technical preit’ll generate different applications this view. Meanwhile, AEC that,” said way… anybody could do it.” firms have for months been Autodesk’s The significance is conexperimenting with third trol: with your own MCPs party read/write Revit Michelangelo you can build the workflow MCPs and have created Capraro you want, and you can also their own MCPs to integrate move the expensive part with Revit’s API. onto your own hardware. Bunszel explained Autodesk’s restraint candidly, and she is probably right to be cautious. “If I Taking the processing local [Autodesk] screw up someone’s Revit In the age of AI, AEC firms are paying model, it’s a little different than if a third- close attention to the economics. Agentic party [tool that] someone downloads and workloads can break cloud pricing, said uses screws up the Revit model.” Young, “The agent can consume tokens a Autodesk’s new “Trusted Write” MCP lot more quickly because the agent is

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and Essentials ships with every Revit and AutoCAD licence. One of the benefits is getting consultants in early — the example given was a structural engineer flagging, before QA/QC, that moving a building off swampy ground could save $5 million.

Total Carbon Analysis Forma Carbon Insights evaluates embodied and operational carbon as the design develops, allowing designers to run analyses directly in Forma Building Design and Revit. The operational carbon engine is EnergyPlus; embodied carbon uses Autodesk’s own. Forma Site Design shows a high-level summary of the same analysis — “the same engine, the same data behind all of it” — while the dedicated tool allows more detailed or custom simulations. As Autodesk

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sustainability expert Indigo Adara explained, it can answer questions in real time, such as “How would

adding solar panels change things, how would changing the HVAC system change things?”

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spawning subagents. “At a certain point the cost of tokens on the cloud exceeds what your IT budgets are,” he said. Symetri’s Kevin Philips told AEC Magazine that it is starting to become very attractive for AEC firms to buy hardware. He explained that the top 5% of developers could be spending as much as $10k a month on tokens, while other practices are not allowed to use frontier models, full stop. In public Autodesk appears relaxed about that prospect. When AEC Magazine talked to Bunszel about firms running open-weight models locally, with no cloud costs, she replied. “That’s great.” She agreed that firms may end up handling parts of a workflow themselves, while relying on Autodesk for others. Running open-weight models on-premise on servers is one approach, but at AU, all three major workstation vendors were demoing truly local alternatives.

experimenting with the ZBook Ultra G3a. Moving beyond a single MCP connection into Revit, Schleusner has been running “very large models or smaller models working together on the device” to handle real production workflows. This includes an LLM to set up Revit projects, machine-learning-driven realtime clash detection, and an image model that checks room contents against requirements. “If your concern is protecting your data from going to the outside world, then looking to a local piece of hardware, a local device is important,” he said.

Autonomous modelling

On the Dell stand, Nvidia showed autonomous modelling for Revit and Fusion, using a Dell Precision laptop connected via Ethernet to a Dell Pro Max with GB10 - a tiny desktop device powered by the Nvidia GB10 Grace Blackwell ‘superchip’, which combines Nvidia’s Armbased Grace CPU and Blackwell GPU on Revit, MCP and LLM – all on one PC one piece of silicon. Revit and Fusion, along with custom HP’s partnership with Perplexity, announced alongside the new ZBook MCP servers, ran on the laptop, while Qwen3.6-27B, an openUltra G3a mobile workweight LLM, ran on station, was the most the GB10. The Revit accessible example of local AI we saw at AU. Autodesk’s real bet demo showed how a third-party MCP could The key is a combinais on context — be used to model a tion of pre-installed softknowing where to basic building from a ware and 192 GB of syspull data from prompt. It was more tem memory, of which a proof of concept than massive 160 GB can be across project allocated to the integratdecisions, disciplines practical solution, but an important point was ed GPU thanks to the and history, and made: firms can finesystem-on-chip design stitching that into tune open-weight modof the AMD Ryzen AI Max Pro processor. something coherent. els using their own Revit data to get better That’s enough GPU That’s harder to results, something memory to run the entire replicate than you can’t do with a CAD and AI software frontier model. stack on a single simply using AI to In contrast, the machine, something that model geometry, Fusion demo was one typically can’t be done and it’s where of the most striking on any other mobile Autodesk is trying things we saw all week, workstation. using multiple agents Perplexity acts as the to make itself to model and refine. orchestrator, with the indispensable The agents were demo showing orchestrated through the Perplexity’s own LLM, a Hermes harness from post-trained version of Qwen, driving the official Revit 2027 Nous Research (hermes-agent.nousresearch. MCP server, all on one machine — and, com), which supports MCP, and unlike as Perplexity’s Daniela Miranda-Smith Claude Desktop, can connect to any model, explained, “if you’re using local models, in this case the local LLM on the GB10. The thing that stood out was the use of the cost is really just electricity.” At the other end of the spectrum, Greg multiple agents and feedback loops: Schleusner, former director of design technology at HOK and regular presenter 1) The user first provides an prompt with a at AEC Magazine’s NXT BLD, has been product design brief and two concept imag-

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es. A project manager agent reads the brief, analyses the concept images, and breaks the project into individual tasks, assigning each stage to a specialist subagent. 2) A CAD subagent equipped with purpose-built engineering skills creates the model in Fusion’s Design workspace, organising components and applying initial materials and properties. The geometry created is verified against the design requirements, checking dimensions before handing it off to the visualisation subagent. 3) A visualisation subagent sets camera position, lighting, and render properties, then compares the resulting render against the original concept images. It automatically adjusts materials, properties, and scene settings in a continuous loop to match the concept images as closely as possible. Once approved, the model is then passed off to the drawing subagent. 4) A drawing subagent produces 2D engineering drawings and a parts list derived from the modelled assembly, checking views, dimensions, annotations, and list completeness. 5) The project-manager agent reviews the outputs. This was just a demo, but consistent with Young’s account of recursive agent work. In his AU presentation he described how an agent could read the design requirements for a product, model the geometry, then run two different types of FEA simulations at the same time. If there’s a problem with one of the simulations the agent loops back to correct the geometry until it gets it right.

Accessible AI Lenovo’s demo was the simplest, but arguably the most relevant to the average AEC firm as it does not require investment in hardware. It showed a custom agentic front end to AutoCAD Civil 3D making cut and fill volume calculations far faster and simpler. The remarkable part is that it ran on current hardware — a Lenovo ThinkStation P3 Ultra with a 16 GB Intel Arc Pro B50, a class of GPU many firms already have in their workstation. Lenovo explained that different agents could reside on different workstations across the network and be called upon by individual users as required.

Licensing: the elephant in the room Running AI locally raises real questions about licensing and business models. If a www.AECmag.com

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Technology

firm builds agentic workflows around workstations or on-premise servers, with no process touching Autodesk’s cloud services, how might this impact Autodesk’s revenue? For example, Autodesk offers the Revit Automation API through Autodesk Platform Services (APS), allowing the Revit engine to run in the cloud and create, extract and modify Revit data without a local installation. If an agent handles all of this locally instead, Autodesk could potentially miss out on that usagebased cloud revenue stream entirely. There could also be an impact on licensed-seats sold. If agents end up taking over bulk modelling, querying, and documentation work that previously required people at seats, a firm may simply need fewer Revit licences to do the same volume of work. Agentic AI workflows could even extend beyond a single application. In his AU presentation Young described agents running “an unlimited number of applications concurrently” via subagents, each driving an application through MCP with no UI interaction at all — the agent “doesn’t need to see the viewport.” It’s effectively a headless API call. When asked about licensing and MCP, Autodesk told AEC Magazine that customers are able to run multiple Revit instances on a single workstation, but the Revit instance must be properly licensed, and the users must be signed in. It also www.AECmag.com

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Autodesk previews confirmed that its public MCP shifts will show up in its next generation enables customers to create Forma first, “and then cusstandalone Assistant agent workflows for their own tomers can choose.” internal business purposes, but developBunszel drew a parallel to Autodesk’s ers looking to build commercial applica- long push to move users from AutoCAD tions can use APS. to Revit. Plenty of people never made That should be reassuring for firms that switch either, and “we’re OK with looking to use Autodesk’s official MCP to that,” she said. automate internal workflows, although Asked how the future standalone the position may be less clear when Autodesk Assistant would decide third-party MCPs are involved. And whether to route a task to Forma or with agentic technology developing so Revit as their capabilities converge, rapidly, firms should nevertheless build Bunszel said the “Orchestrator” behind local AI workflows with their eyes open. that decision would need to be “really Autodesk’s licensing terms may appear smart.” The key difference, she to accommodate these workflows today, explained, is in the inputs: Forma workbut that does not mean they will neces- flows start from something closer to a sarily do so in the future. If agentic AI specification, while Revit workflows begins to significantly reduce the num- involve “a human being writing a ber of seats required or bypass usage- prompt and describing it all.” She added based services, Autodesk could have a that Autodesk plans to “bias working strong commercial incentive to revisit out the kinks” on the Forma side when it those terms. comes to new ways of working. She also offered a glimpse of how Revit Forma will replace Revit could eventually be bypassed altogether. At AU, Autodesk made its clearest decla- Bunszel described starting from a project ration yet about the future of Revit, con- specification in Forma Site Design, firming that “Forma will replace Revit including the intended construction methover time.” But the statement wasn’t od, and bringing that all the way through, made from the keynote stage — it was in many cases skipping over entire parts of what Revit does. In many cases, she buried in the Q&A of a press release. When questioned, Bunszel told AEC said, Autodesk should be able to generate Magazine that “there are better ways of construction documentation “without doing things now,” pointing to granular maybe building the detailed model the data, cloud, and AI. Revit will still get way people do it today,” with costs investment, she said, but new paradigm tracked automatically along the way.

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Technology client requirements, and all of my Forma applications will know what those design targets are,” explained Poon. For Forma Building Design, three features landed on the Friday before AU: indoor daylight analysis, which uses window size, ceiling height and room depth to answer questions such as “is this building too deep for a compliant double-loaded corridor”; indoor parking; and a furniture library.

Conclusion

Autodesk is exploring new ture. Last year Revit became For now, though, while ways to interact with its a Forma Connected Client. Forma is built out, Revit tools: Project Quill combines Now it’s the turn of Civil 3D, remains Autodesk’s primary sketching, speech and AI which as Theo Agelopoulos, BIM tool for detailed design and is also gaining new AI-powered VP, AEC design strategy, explained, allows “Civil 3D design models to work workflows. closely with Forma and Revit, so that Automated annotation planners, architects, and civil engineers The Revit AI feature that drew most can work in a more coordinated way.” Automated subsurface modelling is attention was optimised annotation. Rather than having to reposition each also coming to Civil 3D, building a 3D annotation manually after a change to ground model “in minutes” and sharing the model, the user can now ask Revit’s it into Esri ArcGIS. Underpinning all this is a new unified built-in Assistant to optimise all tags in a view, and review. “Everything the coordinate system across Forma, Revit Assistant suggests will be in purple, and and Civil 3D, with more products to come I can manually adjust anything I’m not - a customer ask that had been outstand100% happy with, so I still stay in con- ing “forever,” in Bunszel’s words, and an trol,” said Lauren Poon, senior product announcement that drew one of the biggest cheers of the keynote. line manager. Meanwhile, Autodesk also announced Bunszel explained that the AI model that Autodesk has built for this can Forma Street Design, a new parametric already do other things. The end goal is solution for streets and intersections, fully automated documentation. “Fusion taking Autodesk further into early transhas a bunch of that, so it’s a natural next port planning. Teams can explore lane configurations step for us to work on,” adding that for the technology to be most effective, the and intersections in a real-world context, technology it would need to understand a test options, then carry the design directly into Civil 3D for detailed design and company’s internal standards. With the help of AI, this development documentation “rather than starting over could come sooner rather than later. As and recreating work.” Forma Street Design will sit alongside Anagnost shared in his press Q&A some of the microservices demoed on stage Autodesk’s other early-phase tools: Forma came from having Claude “ping Revit’s Site Design and Forma Building Design. Autodesk also showed off new AI capacodebase and ping Revit’s APIs.” For some things, he said, “what used to be bilities for Forma. First, Building Layout like a five-year project became a five- Explorer, a generative AI capability in Forma Site Design that helps teams genweek project.” erate and evaluate floor plan options from Forma moves upstream and sideways a massing model, and second, Forma Autodesk has now pushed Forma Compass, which allows users to import upstream and sideways into infrastruc- project briefs into Forma. “I can input my

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If there’s one takeaway from AU 2026, it’s that Autodesk is shifting its value proposition from tools to outcomes. Assistant is shaping up to be the thing that decides, on your behalf, how to solve a task — not just a faster way to use Revit, Fusion or Civil 3D. But Autodesk is chasing that ambition against a moving target. As we’ve seen recently with OpenAI’s GPT-6 Astra, frontier models have overnight become very proficient at modelling, and that will only get better. And, as Nvidia demonstrated on the Expo floor, modelling can also go fully local, with the added appeal that firms can train open-weight models on their own data while retaining full control over their IP and costs. Modelling, though, is an isolated process. Autodesk’s real bet is on context — knowing where to pull data from across project decisions, disciplines and history, and stitching that into something coherent. That’s harder to replicate than generating geometry, and it’s where Autodesk is trying to make itself indispensable. The big question is whether customers actually want that from Autodesk, especially when they don’t really know how much it will cost now and especially in the future. As we have already seen, some AEC firms may prefer to build their own in-house stack — their own workflows, their own design automation, their own connections between applications — keeping data and costs firmly under their own control. But even that route isn’t free of uncertainty. If your new workflows aren’t properly formed, how can you predict how many GPUs you might need, or even if current licensing costs will remain? The good news is that, for now, Autodesk has only shared its vision. The next-generation Assistant isn’t expected until sometime next year, which gives AEC firms time to work out the best approach. However, with AI technology changing on a weekly basis, it may be the hardest time yet to be the IT, BIM or R&D director at an AEC firm. ■ www.autodesk.com/ai

www.AECmag.com

08/10/2026 14:34

Is Your Organisation AI-Ready? Connected, well-structured data is the foundation for AI By Ben Porter, Co-founder, Remap In the built environment industry,

describes its data consistently.

The real value comes from pairing

access to the same AI tools. What

timesheets and documents are

source of truth – the data core.

kept up to date automatically. Our

Governance from day one

almost every organisation now has

sets organisations apart is what

those tools can see. Years of project knowledge sit scattered across file

servers, finance systems, CRMs and BIM models, and AI can only be as

useful as the data it can reach. Read on to find out how we help organisations get ready.

Projects, clients, people, fees,

defined once, linked properly and

own data lake at Remap connects our finance system, our sales

pipeline and our projects server, so we can ask questions across all three and build charts and

dashboards on the fly. It's still

growing, but it has already changed

Disconnected data

how we run the business. Questions

So many organisations are

spreadsheet wrangling now take

developing useful AI tools but

reaching a ceiling. When we dig in, the tool is rarely the problem. The issue more commonly lies in the tools access to well-structured

business and project data. The tools

rely on export / import of information and operate in silos of very specific

functionality. The data sources exist,

but they are currently stored in

systems that don’t talk to each

other and therefore unavailable to be leveraged by AI.

that used to need an afternoon of

seconds, and most importantly we're making decisions on live data.

Connecting AI to your core Once the data is connected, AI

becomes far more useful. Emerging standards such as the Model

Context Protocol (MCP) make it

possible for plain English questions

to be answered from your own data - projects, bids and client history. A

bid team can pull together relevant case studies and CVs in minutes. A

Start with a data core

project director can create a job

The foundation we recommend is a

three separate reports. Leadership

data core: a single, well-modelled

layer that connects the systems an

organisation already relies on and

health dashboard without chasing can explore trends across the whole portfolio and see the results

visualised exactly how they want.

capable AI tools with a trustworthy

Connecting AI to business data

raises fair questions. Who can see what? Where is the data stored?

What happens when an answer is wrong? These questions need designing in from the start.

Permissions should mirror the ones people already have, so nobody gains access to something they

couldn't open before. Data residency matters too, particularly for

practices with offices in more than

one country. We recently proposed exactly this approach to a multi-

office consultancy: a short discovery phase that maps their systems,

models the data core and produces a pattern book, followed by a

phased build of the data core itself.

Small steps, lasting value Getting AI-ready doesn't require a

big-bang transformation. Start with the questions that matter most to

the business, connect the handful of

systems that answer them, and build outwards from there.

At Remap, we help organisations

turn scattered data into connected, reliable foundations for AI and

better decisions. We bring together deep AEC domain knowledge, data

engineering and a practical, phased approach that delivers value early. Whether your data is already in

good shape or you're not sure where to start, get in touch, we'd love to

show you what it could do for you.

Remap's data lake brings finance, pipeline and project data together in one platform

p47_AEC_SEPTOCT26_Remap.indd 1

Visit:

www.remap.works

Reach out:

hello@remap.works

08/10/2026 13:47

Software

Compliance software in AEC Two years ago there were a handful of compliance tools helping AEC professionals navigate building codes and regulations. There are now at least nineteen. Allister Lewis looks at what is available, what these tools can and cannot do, and what happens when one of them misses something

R

ule-based checking software own internal standards covering naming, solely for architects. Several are aimed at has existed for years in AEC numbering and schedule alignment. All of contractors, where submittal and Request but it required someone to these are documents and all of them get For Information (RFI) volume is highest translate written regulation described as compliance issues however and the business case is easiest to make. into machine readable rules and then only the first is enforced in law. This is important as a tool that can read a The distinction that matters when drawing set could be used by any actor in maintain them. That changed with Artificial Intelligence. AI models can assessing this software is between pre- the construction process. For example, a read drawings, models and regulations scriptive and performance requirements. contractor can now review one of your as they are actually written and compare The Building Regulations set out func- packages, and so can a client prior to signone against the other, without that trans- tional requirements and the Approved ing anything off. Intriguingly, Building lation step. That is a major reason why Documents are guidance on one way of Control could start to use these tools too. compliance has become one of the fastest meeting them. The requirement is the The opportunity with these tools looks law and the guidance is not. Section 7 of different depending on who is applying growing categories in ConTech. Compliance matters to clients and con- the Building Act 1984 states that failing it. For an architect it is a way of checking tractors because they need the design to follow an Approved Document does information before issue so that risk durinformation to meet the project require- not of itself make anyone liable, although ing construction is reduced. For a conments. Where a building is not compliant it can be relied on as evidence that the tractor, checking consultant information the result is delay, additional cost and regulations were not met. You can com- and being able to push back, query or increased risk, and raise additional RFIs resolving that early carries a direct finanleads to better outcial benefit and variaThe return on investment may rest less on comes on site. This tions can be moniarticle looks at what tored in the same way. productivity than on the reputational cost of compliance actually discrepancies revealed externally. To be clear, finding For a client it is the covers, what the ability to check that it first could be worth more than just finding it faster what has been issued options are and why this is a critical matches the brief and moment for consultthe local regulations, ants, clients and contractors to consider ply by another route if you are able to and to interrogate information without demonstrate that the functional require- being an expert. what this shift means for them. ment has been satisfied. That leaves a question for AEC profesWhat compliance actually means Where a requirement is prescriptive, sionals: should we be checking our own Compliance means meeting a requirement meaning a dimension, a U-value or a work before issue or waiting until a conthat somebody else has set, and being able specified product, it can be tested against tractor or client does it for us? Compliance to show that you have met it. There is a a drawing or a schedule and a computer checking may happen regardless of the requirement, there is evidence that it has can now do that better than ever before. architect performing this and it is probabeen satisfied and there is somebody with Where it is a performance requirement, bly better to identify issues before an such as adequate means of escape for the external party does. Reviewing internally the experience to check this outcome. On a construction project those require- intended occupancy, demonstrating it first with software, followed by a thorments arrive from several directions at takes modelling, judgement and a com- ough human review, appears to be a useonce. There are statutory requirements, petent person to sign this off. Each one of ful combination. The first pass identifies which in England means the Building the 19 tools across the page is working the easily highlighted mistakes and the second is where a skilled professional Regulations and the functional require- the prescriptive side of that line. focuses on the issues that matter. Catching ments set out in Schedule 1. There are conthe simple errors early saves time when a tractual requirements, for example Who is doing the checking? Employer’s Requirements. There is the The list of tools is not comprehensive, but package runs to hundreds of drawings. client brief and there are also a practice’s it is interesting that only a few were built The return on investment may rest less on

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08/10/2026 14:45

Compliance for AEC: 19 tools, 4 broad categories Several of these sit in more than one category. Structured AI works on native Revit models as well as PDFs and could also be placed in Group 1. Uptocode covers code research alongside package review. The four categories are a way of locating where a tool might sit in your own process rather than a fixed classification.

1

Inside the BIM authoring tool Requires a BIM authored model of high enough quality to interrogate

3

Drawing sets and PDFs No BIM required, which suits an industry that still demands PDF deliverables

AI-BOB does continuous verification against models, covering area compliance, accessibility and BIM execution plan checks.

Helonic works from 2D PDFs across cross-sheet consistency, code, schedules, references and specs in a single pass.

■ www.aibob.io

■ www.helonic.com

Kestrel Labs runs checks natively in Revit, tying results to the model element and citing the code section.

MarkedUp uses computer vision for sheet index audits, callout verification and schedule sync, and positions itself as a final audit rather than a first pass.

■ www.kestrellabs.com

■ www.markedup.ai

Pirros runs its quality management review on the Revit project that is open and surfaces the findings back in the model rather than in a separate report.

Archiboost combines code compliance and QA/QC with a firm knowledge hub that draws on past project detailing.

■ www.pirros.com

■ www.archiboost.ai

Baumind reads regulations, checks 3D geometry and auto-generates corrections.

Freeda pairs AI with expert human review, returning a report in hours, and operates across Europe, North America and the Middle East.

■ www.baumind.space

■ www.freedaai.com

2

Archie connects to Revit, Archicad and BIM 360 and holds meetings, notes, regulations and design changes on one timeline, with automated code checks alongside.

Uptocode covers drawing, BIM model and PDF review alongside documentation and specification intelligence, and extends into code research and reference.

■ www.archiecodes.com

■ www.uptocode.ai

Tektome reviews a whole package against statute, the client brief and a practice’s own standards, showing each risk with a citation.

Documents and specifications

■ www.tektome.com

Aimed at the sheer volume of information a project produces

Structured AI is a QA/QC platform for drawing packages that works across both PDF drawings and native Revit models.

CODiii connects to Google Drive or SharePoint, classifies project documents automatically and runs conflict detection and gap checks with no file migration.

■ www.getstructured.ai

■ www.codiii.com

Buildcheck reviews drawings with the aim of reducing authority comments, RFIs and change orders before they arise.

Specset, formerly Specbook.ai, cross-references specs, drawings, RFIs and submittals as one system and integrates with Procore and ACC.

■ www.buildcheck.ai

■ www.specset.com

Nomic checks drawings against what it puts at more than 380 building codes, grounded in your own project documents. ■ www.nomic.ai

4

Code research and reference No project data required, so more of a pre-compliance check on the requirements. The easiest place to start

Ichi handles submittal reviews and document checks at scale, with a persistent memory layer for jurisdiction rules and client preferences.

UPCodes is a searchable code platform now connecting codes directly to specifications through its AI-native spec module.

■ www.ichiplan.com

■ www.up.codes

Struck supports regulation lookup and permit checking with cited answers, built on a filterable library of building codes spanning several countries including England. ■ www.struck.eu

www.AECmag.com

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Software Kestrel Labs runs checks natively in Revit, tying results to the model element and citing the code section

productivity than on the reputational cost of discrepancies revealed externally. To be clear, finding it first could be worth more than just finding it faster. The ability to load your own project requirements is also more important than it first appears. Building Regulations get read, checked and referenced by someone on every project. A two-hundred-page Employer’s Requirements document, issued at stage two and referenced in your appointment, frequently does not get read by the whole team and that is precisely where contractual risk could be caught earlier.

an owner responsible for resolving it, a record of what was decided and why, and a review at the end that somebody signs off. The same discipline may apply here, and a compliance report that arrives without that process is a document that has just been received, rather than a check that has been carried out. The evidence of a review is often what may be required later rather than just a review in itself. If a question is asked about a decision two or three years after handover, what protects you may be the record showing that the issue was raised, considered by a named person and resolved for a stated reason.

What if it misses something?

Choosing a tool

A clear risk area with compliance software is when it misses something. Professional judgement, and reasonable skill and care will always be required and understanding that this is a tool like any other is important. Using one of these may cover the first and simpler review of issues but a thorough look will still be needed to confirm that internal and external standards are being met. None of these products carry professional liability and a review of their terms and conditions is worth doing. None of these tools will be named in your contract, which means the output always needs to be questioned and queried. As a comparison, this may emerge into a specialism not dissimilar to clash detection/avoidance and the process for handling outputs already exists in most practices. Clash detection analyses similar problems, which is a long list of findings, some of which matter and many of which do not. It gets a status against each item,

The obvious question is which of these is best for your practice? However, the more useful question is where your own process currently has the least visibility in regard to technical issues or where it absorbs the most time. Architects spend hours reviewing and checking information and in most offices that work is carried by a relatively small number of experienced people. What that means in practice is that a package may go out having been checked only by whoever was available. Once you know where the gap is, writing a short brief setting out what is required from the software is needed. The group it falls into will narrow the field considerably, and you can test what remains through demonstrations, trial use and question and answer sessions with the vendor. The most useful test is to run it against a completed project where you already know the answers, ideally one where something was picked up late, and to measure it by what it

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missed rather than what it found. Ask four things before deciding: which regions, jurisdictions and editions are covered; how quickly coverage is updated when an amendment is issued; whether the output is auditable by someone who was not in the room; and where your project data is stored.

Where this ends up There may not be nineteen options of these tools in two years. Some will be acquired, some will be absorbed into software you already pay for, and some will stop through a lack of funding or users. That is normal for a category at this stage, and it means maturity and longevity are as much a part of the decision as features are. A tool that fits your workflow today may still be the right choice, but it is worth knowing what the alternative would be if it disappears. What is not in doubt is the direction. Checking will be automated, either by you before the information is issued or by whoever receives it afterwards. The gains are real and worth exploring early, in speed, in confidence in the deliverables, and in knowing that a package has been reviewed to the same standard every time. Just do not mistake the tool for absolution. Professional responsibility still sits with you. Allister Lewis is an architect and BIM manager, and founder of ADDD, an independent platform that helps architecture practices make better technology decisions. It includes a comprehensive ConTech Database for AEC that now holds close to two thousand tools. ■ https://app.addd.io

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08/10/2026 14:45

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08/10/2026 16:31

AI’s next bottleneck is the engineer’s desk Nomic is helping Arcadis automate engineering reviews, but AI’s next bottleneck may be the senior engineers still responsible for checking them. Martyn Day spoke with Nomic CEO Andriy Mulyar and Arcadis global AI enablement director Freek Matheij to learn more

A

ndriy Mulyar, founder and CEO of Nomic, recognises the problem inside his own company. As AI accelerates software development, he says the constraint becomes “the mental bandwidth of our senior engineering staff’s ability to review things.” He applies the same reasoning to global design and engineering consultancy Arcadis, where Nomic’s software supports engineering reviews: preparing the work takes less time, but accepting responsibility for it remains a human job. I was introduced to Mulyar by Freek Matheij, global AI enablement director at Arcadis, who had contacted me the previous week to discuss the firm’s AI strategy. I had expected another conversation about Microsoft Copilot, but Matheij was far more enthusiastic about software that could work directly with project and asset data. “Nomic is maybe not the most sexy AEC platform out there, but it really helps in terms of everything in the asset life cycles,” he explained. Drawing review had helped get adoption of Nomic moving, with applications extending into specifications, tenders and submittals. Nomic describes a first pass through drawings, specifications and models, each finding linked to its source. Matheij’s priority was accuracy on large, complex documents and drawings, though he said no outcome can be fully trusted: engineers decide what matters.

The Arcadis interest Arcadis is not just a customer. It has backed Nomic with a strategic investment and long-term commercial partnership, so its endorsement is not disinterested. Arcadis reports that 86% of participants in its six-month, 12-country trial said Nomic changed how they approached their work. At its Capital Markets Day in September 2026, it said Nomic had been 52

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rower task than assessing whether a proposed product meets the requirements, but it puts Autodesk in the same workflow and in front of the same customers. Procore is a more direct comparison. It acquired Datagrid in January 2026, explicitly identifying autonomous submittal review, RFI drafting and access to information across different systems as part of the rationale. In May 2026, it announced agents powered by Datagrid and embedded within Procore, including submittal and contract-review capabilities. Nomic’s proposition of agents working across project information is therefore also being pursued by a vendor with an established construction-management customer base. Nemetschek’s Bluebeam has also bought talent and technology from mbue, a startup specialising in drawings, submittals and construction AI. Alongside the platform vendors are companies such as Togal, concentrating on detecting, measuring and counting from drawings. There is competition for both the individual tasks and the broader project workflow. Mulyar sees distribution as a major challenge. Getting the technology to work is only part of the job; Nomic must then get it into firms and across their projects. The company offers individual licences as well as company-wide deals, giving AI researchers inside firms a way to try it. Alternatives such as Procore already have customer relationships and a place in the daily workflow. Nomic has to earn that access, establish trust and perNomic CEO suade engineers to Andriy Mulyar adopt another product, which

used in three winning technical tenders with contract values above €25 million. While the number of participants in the initial trial was only 150, representing less than half a per cent of a workforce (Arcadis is huge with 34,000 people), Nomic is now official enterprise software anyone can request, Matheij says, with licensed users up to almost 500 since July and more than 750 workflows built, about half of all runs inside project workspaces. A leaderboard, he says, rewarded use on project work and reuse of colleagues’ workflows, to change behaviour, build AI literacy and keep spend tied to project budgets. The trial was a reason to expand, not a measure of what the entire business has gained. Arcadis was not starting from nothing. Its people already use Microsoft Copilot, and some teams work with Copilot Cowork. Last month it announced an expanded collaboration with Autodesk, and it lists Nomic among its focused partnerships alongside Microsoft, Autodesk, Bentley and Nvidia. Matheij describes Copilot and Nomic as complementary: a productivity agent and an AEC project agent. Nomic works within platforms including Autodesk Forma and Microsoft Sharepoint, with Bentley Systems integration in progress.

A crowded engineering inbox Nomic is entering an area the established AEC software vendors have been working on for years. Autodesk acquired Pype in 2020, bringing automation for submittals and closeout into its construction portfolio. Pype AutoSpecs extracts requirements from specifications and generates submittal logs. That is a nar-

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Technology

1 IFC view of a medical clinic

1

2 Agent querying model elements

makes the Arcadis relationship valuable well beyond the investment. It also helps explain Nomic’s potential appeal to an acquirer with established distribution: the buyer would gain both a working product and a team with specialist engineering and AI experience. Its independent position gives customers a choice. Arcadis can use Autodesk’s authoring and project systems without taking all its engineering AI from Autodesk, while Nomic can work with information connected from several sources. But choosing a different AI supplier does not remove dependence on the systems holding the data. The usefulness of that choice still rests on what those systems’ connections and permissions allow an external agent to reach. Arcadis’s parallel investment in Nomic and expanded collaboration with Autodesk illustrates how this can work in practice. A firm can retain its existing tools while looking elsewhere for the software that checks and coordinates their output. For Nomic, the commercial test is whether the quality of that work justifies another product alongside the platforms customers already pay for.

An earlier encounter with AI Mulyar’s route into AEC began elsewhere. An early engineer at RadAI, he trained multi-billion-parameter language models to assist radiologists and was also a PhD student at NYU’s Courant Institute of Mathematical Sciences. The radiology work showed him how specialist data and professional expertise could make AI useful within a demanding workflow, without requiring it to perform the whole profession. Nomic, where Mulyar previously served as CTO, was founded in New York in 2022. Its early products included Atlas www.AECmag.com

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2

and GPT4All, followed by open embedding models, reflecting his interest in making AI and its training data more accessible. The company raised a $17 million Series A led by Coatue in 2023, before turning its attention towards AEC. That change of direction was not simply the discovery of a promising market. Mulyar said revenue in the earlier business had flattened as capabilities Nomic sold became part of the foundation-model providers’ offerings. “The product we were building got consumed by the foundation model companies into their API layer,” he explained. Nomic had already experienced the threat facing many AI startups: the next model release can absorb what used to be your product. The search and retrieval work nevertheless had an application in engineering. “Our models can find the right pieces at the right time at the right moments,” said Mulyar. A question about a project may require only a handful of details, but those details can be scattered across thousands of pages. Finding them and preserving their context is a large part of getting a useful answer.

Whose drawings are in the dataset? Drawings introduce problems that ordinary text retrieval does not solve. Dimensions, symbols, geometry and notes have relationships that need to survive interpretation, while references lead to other sheets and requirements elsewhere in the specification. A model that can describe a plan convincingly may still be unreliable at measuring it or following a detail through the document set. Mulyar described one source for Nomic’s development material: licensed datasets purchased from commercial vendors, including information originating from construction businesses that are no longer operating, alongside historic project packages and correspondence. Nomic then employed domain specialists to make the material useful. “We hired internal structural engineers, mechanical, electrical, plumbing engineers, architects that literally sit there and help us strip mine the data and make sure it’s a high enough quality to build really good evaluations from,” said Mulyar. The work involves establishing engineering tasks and acceptable answers, giving the September / October 2026

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Technology developers something against which to measure the system. This is more involved than cleaning drawings and feeding them into a training run. The team needs to know what information a task depends on and what constitutes a correct result. Otherwise, changing the model or adding another tool may produce a more convincing answer without producing a better one. Nomic has published part of its approach through AEC-Bench, covering drawing understanding, reasoning across sheets and project-level coordination (www.tinyurl. com/AEC-bench). The release includes the dataset, agent harness and evaluation code. It is a vendor-authored benchmark rather than independent certification of the product, but it makes part of the company’s technical approach inspectable. The harness supplies the tools, procedures and context around the underlying AI model. Nomic uses frontier models within that system while developing specialist capabilities of its own. Mulyar says enterprise customer data is excluded from model training, distinguishing the information a customer supplies for a live project from the material Nomic procures for development.

The value in submittals For Arcadis, Mulyar identifies submittal review as a particularly strong area of expansion. A contractor’s proposed equipment or materials must be checked against the project’s requirements, which can involve navigating drawings, specifications and supporting documentation. “That’s a place where they really trust it,” he said of Arcadis’s use. It is also, in his account, releasing significant engineering capacity. Matheij sees the benefit of having the relevant information assembled around the project. “It’s kind of a knowledge database based on projects, and then you can have the right context, and that makes it very cheap to ask questions,” he said. The engineer spends less time searching for requirements and more time dealing with the findings. How that becomes a financial return depends on the contract and the work available. Released capacity could support more projects, more attention to design, or more time with clients. The commercial effect will differ between hourly billing, fixed-fee commissions and percentagebased fees. Fewer hours spent producing or reviewing work do not automatically translate into higher revenue or margins. Mulyar also described agents periodically checking connected project information for changes and initiating tasks with54

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out waiting for a prompt. In the submittal developing its own large language model example, he said, “we can kick off those for project delivery, drawing on the spethings autonomously before the human cialist data and engineering tasks it has even sees the email, and then they just get assembled. That would extend its work the end document to review sitting in the beyond providing tools and context SharePoint folder.” That changes the around other companies’ models, allowengineer’s starting point from a new ing it to address some of those tasks request to a prepared review. through training the model itself. The It is also how the queue at the senior work is still in development, with coordiengineer’s desk could fill up. A person nation and review the immediate focus asking an assistant to perform a task con- rather than automated drafting. trols when the work begins; background That willingness to replace its own agents can prepare several reviews while technology matters in a market moving that person is occupied elsewhere. The this quickly. Nomic’s earlier experience benefit depends on how much checking shows how improvements elsewhere can those outputs still need. Producing them undermine a product, while its internal overnight is useful, but it does not create development can also make previous extra hours in the reviewer’s morning. work redundant. The continuing job is to Matheij’s account placed greater evaluate what works, remove what is no emphasis on engineer control: “Always longer needed and concentrate on the human in the loop.” The interviews leave engineering tasks that remain difficult. open exactly which background operations are enabled across Arcadis, but the Conclusion distinction practitioners need to make is The quality of what reaches the engineer’s clear. Permission to start a check is differ- desk will determine how much time AI ent from permission to change a design or actually saves. A long list of weakly eviapprove a contractor’s proposal. denced, poorly prioritised issues could There are also limits to what any project consume much of the time saved in preagent can know. When I asked how paring it. A review that identifies the Nomic could operate across all the compa- important issues, links them to the relenies involved in a buildvant drawing and ing, Mulyar acknowlrequirement, and makes edged that “you can never uncertainty visible has a have full understanding better chance of reducing A question of the entire system.” Its the total workload. about a project job is to improve the view That also changes what may require available through the firms should measure. The only a handful of information an organisatime taken to generate a tion can access. He said review is only part of the details, but those direct communication calculation; the time taken details can be between separate compato check, correct and scattered across nies’ Nomic agents was approve it determines not something the prodwhether the work has thousands of uct currently provided. actually moved faster. pages. Finding Senior engineers may also them and What comes next find themselves dealing preserving their with a higher concentraDrafting is outside tion of difficult decisions Nomic’s immediate focus, context is a large once the routine preparawhile takeoff is an area part of getting a tion has been removed. Mulyar wants to push useful answer Arcadis gives Nomic a further. He believes specredible foothold in that cialist model developprocess, while its investment can improve results beyond what the harness achieves, but ment gives the vendor a customer with an acknowledged that the strongest general interest in shaping what follows. The models were not yet producing the quality wider market is already competing for a construction team would need to rely on. the same work, with established platGetting most of a drawing right leaves forms acquiring specialist teams and adding agents of their own. For all of them, plenty of scope for an expensive mistake. During our conversation, Mulyar the useful test is how much effort remains showed an older layout-understanding before an engineer can confidently sign approach, then explained that Nomic was off the result. That is where faster AI removing that layer from its production must become faster engineering. stack. It turns out that Nomic is also ■ www.nomic.ai

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THE BEST DESIGN EXPERIENCE Design smarter. Build better. Together. Design projects of any size with Archicad’s powerful built-in tools and user-friendly interface that make it the most efficient and intuitive BIM software on the market.

MJE House, Jacobsen Arquitetura, Brazil jacobsenarquitetura.com, Photo: ©Leonardo Finotti

graphisoft.com

Giving AI actual write access in Revit Can AI move past querying Revit models and start actually building inside them? Chun-Lin Peng, Associate AIA and an architectural designer at Leuterio Thomas Architects & Engineers, tests Claude + MCP + pyRevit to find out

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sk most architectural firms what “AI in Revit” means right now and you’ll get some version of: faster tags, cleaner schedules, maybe a chatbot that explains what a parameter does. This is useful but also falls short of what the phrase truly promises. I wanted to test the promise directly and explore whether AI can actually write to a model, not just read it. What follows isn’t a success story. It’s a record of what that chain got right (ceilings, walls, windows, creating worksets), what it got wrong (stair dimensioning, a rebuild of Mies van der Rohe’s Farnsworth House that’s recognisable but not usable), and what the gap between those two tells us about where this is actually headed. I started where most people testing AI in Revit start: with what Autodesk ships. Revit 2027’s built-in Autodesk Assistant is fine for a narrow band of tasks — creating schedules, tagging rooms, doors, windows. But it can’t touch anything that already exists in the model. In my testing, it couldn’t even rename a schedule it had just created. The official MCP server extension tells nearly the same story: it’s good for query56

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The setup The Claude g MCP g pyRevit g ing model information and pulling it into an LLM like Revit API chain — including the MCP The chain has three parts, server’s local port and the HTTP Claude to reorganise or reeach doing a distinct job. handoff to the Revit API present, but it’s read-only Claude is the reasoning against the model itself — it can reformat engine — it works through what needs to data, but it builds nothing. happen in plain conversation. MCP is the That’s the comparison that actually standardised layer that lets Claude call matters here: both official routes save external tools. pyRevit is the execution time on schedules and tags, but neither environment that sits inside Revit and touches actual geometry. For real calls the native Revit API directly. Put project work, that’s a small dent, together: a prompt goes to Claude, Claude not a shift. The useful version of goes through MCP, MCP hands off to AI in our field is one that actu- pyRevit, and pyRevit executes against the ally produces the model — Revit API to produce the model output. walls, ceilings, stairs, complex The idea for this chain came from two parameter chains — through demos I came across on YouTube: “Revit + natural language, at a level Claude AI Connector with NonicaTab” by that can become deliverables. BIM Pure, and “AI in Revit: Claude + MCP That’s what pushed me to pyRevit 100% free” by BIM Coder — which look for another route: an I then extended into the testing below. open-source toolchain The difference from the official tools is combining Claude, access level, not just capability: this route MCP (Model has full API access, not read-only. Context Protocol), Anything a Python script or C# add-in can do to a model, Claude can now do and pyRevit. www.AECmag.com

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Technology

est result: a lot needs corwalls, windows, ceilings; Test model with several AI-generated/ edited elements shown together — recting, but you can clearnew floor and wall types; room-by-room ceilings and finishes, ly tell it’s the Farnsworth filters, colour codes; crean edited floor type, a new roof, and House. That’s not deliverating worksets and an adjusted scope box (green dashed lines). Not every capability tested is able, and I want to be assigning elements to the visible in this view direct about that. But it’s correct one; floor plans also genuinely notable and reflected ceiling plans. Ceilings are the clearest case — that drawings-in, model-out works at all, “create the ceiling for each room at Level on a first attempt, with no training specific X” finishes fast and correctly, and across to this building. Better source drawings multiple rooms it’s faster than doing it by will help, but the bigger lever seems to be hand. This isn’t a demo. It’s real produc- sequencing: giving Claude a structured tion time back on a task nobody particu- pipeline to follow — starting with grids and levels, then working outward, much larly enjoys doing manually. closer to how an architect actually builds Where it struggles up a model by hand — rather than one I tested whether Claude could read 2D open-ended instruction. Early results with drawings and produce a model from them, that approach are more accurate, but still using Mies van der Rohe’s Farnsworth not clean enough to call deliverable. The What works now House as the test case because it’s about as prompt is the next thing that needs refinThese run today from a plain-language simple as a building gets — plans, eleva- ing, not just the drawings going in. For instruction, no custom skill required: tions, and sections in, model out. The hon- existing-building projects that rely on legacy record drawings, maturing this could eventually cut a lot of hours out of some of the least rewarding modelling work we do. Task Revit 2027 Native AI Claude + MCP + pyRevit Stair dimensioning is a different kind of hard. Ceilings are one instruction; stairs Schedules & tags Built-in, fast Fully supported are dense with parameters that all depend Model querying Read-only Supported on each other — run direction, width, tread and riser dimensions, landing size, Geometry modifications Blocked Full write access where the annotation actually lands on the sheet. Getting one of those right in isoType & view creation Blocked Fully supported lation is easy. Getting all of them right together, matching how we actually docuWorkflow automation Hardcoded Reusable “skills” ment stairs, is not. What’s working is dynamically through conversation. Two things worth flagging about getting this running. First, I had Claude write its own connection guide — the setup documentation was partly selfgenerated, which tells you something about where the actual bar for entry sits. Second, some of the setup steps look like real programming, and they are, but once Claude has access to the working files it can carry out those steps itself. The barrier is lower than it looks, not non-existent. The piece that matters longer-term is what Claude calls a “skill” — a reusable instruction set, trained once and reused identically in every future session. That’s the difference between a one-off experiment and something repeatable.

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decomposing the task into smaller trained skills rather than one large instruction — and that decomposition, more than the stair output itself, is the actual finding. It’s the template for how the harder modelling tasks get trained going forward. I’ve also started testing whether Claude can go a step further and build the interface itself — having it write a small Revit add-in that surfaces these trained skills as buttons in a ribbon tab, rather than something you have to ask for in conversation each time. It isn’t debugged yet, but the skills are already showing up where they should in the interface, which is enough to convince me the direction is right. If that holds up, it points to something more useful than a chat-based tool: skills that run independently inside Revit, with no need for Claude to be running at all. I’m currently benchmarking time and token cost against manual modelling for the same tasks. That work is still in progress, so I don’t have numbers to report here — but it’s the next thing I want on the record, because “real time savings” needs a figure behind it eventually, not just my impression of it.

The strategic takeaway Even with everything here still unfinished, it already reduces modelling time 58

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on the simple repetitive layer — and the ceiling on what it could eventually do is considerably higher than what the official tools are aiming at.

After that, the direction I want to test is compliance checking rather than just modelling. Stairs are the obvious case again: width, head clearance, landing size, riser and tread dimensions, plus conflicts against structure or MEP. The aim wouldn’t be a one-off check for a single project’s stair, but something universal — the same check run against any stair, with a clear, consistent output format for whoever has to act on the result. That still doesn’t settle the more important question, though: if AI ends up doing a real share of modelling work, does architecture converge — everyone drawing on the same trained patterns, the same skills, the same defaults — or diverge, because the hours that used to go into repetitive modelling get spent on design instead? I don’t have an answer. I’m not sure the industry does yet either. But it’s the question worth asking now, while tools like this are still rough enough that the answer isn’t locked in.

The Claude + pyRevit interaction driving the Farnsworth House test — including a mid-process correction to the terrace massing, shown deliberately, since catching and fixing a wrong assumption is part of the honest result

Where this fits in the wider landscape I did a scan of what else exists in this space before deciding pyRevit was the right route. The commercial AI extensions built for Revit right now are mostly aimed at framing and refining documentation — helping write specs, clean up sheet notes, that kind of thing — not direct modelling. There’s also a growing open-source scene doing similar Claude-to-BIM connections, which suggests this isn’t a oneoff approach so much as an early, stillforming category. Worth watching rather than treating as settled.

Where this goes next The next test, and probably the highestvalue one, is families — whether this can build usable families from shop drawings, or add parameters that make existing families more flexible. Closer to done is the add-in itself: finishing the debugging so the trained skills run independently inside Revit, with no need for Claude to be running at all.

■ https://pyrevitlabs.io

AI is moving fast! This article was written before GPT Astra (see page 18) and Autodesk announced forthcoming new capabilities for Autodesk Assistant and released a preview of Revit MCP Server ‘write’ (see page 40). www.AECmag.com

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Technology

Revizto: putting the ‘I’ in BIM to work Revizto now has a Model Context Protocol (MCP) server, but more interestingly packaged it with reusable Claude skills by learning what project teams actually do. Martyn Day spoke with CPO Marc Schuetz about getting more from BIM data without giving customers another job — or another bill.

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dding an MCP server is user experience people than product becoming another item on the managers”, and wants everyone involved AEC software developer’s in development to understand the jobs checklist. Connect the applica- customers are trying to get done. He also believes customers should tion to an AI assistant, demonstrate some natural-language queries and announce choose their own AI tools and expect softthat customers can now talk to their pro- ware suppliers to fit around them. “It’s jects. After watching a few of these dem- your process; it either fits in or it doesn’t.” onstrations, I usually have the same ques- Increasingly capable customers are already setting their own pace. Recalling tion: what are the actual benefits? a Foster + Partners presMarc Schuetz has been entation, Schuetz asking much the same described his reaction: “If thing. Revizto’s chief we as the software venproduct officer, whose dors don’t get our shit appointment was right, they will do it announced in January themselves. They are 2026, previously worked doing it themselves.” at Bentley Systems and PTC. He described watchBeyond the connection ing compressed videos of lengthy AI tasks and AEC has no shortage of wondering whether the information. We create work could have been models, attach properties, done faster conventionalgenerate drawings and ly. “What’s the benefit of record coordination We have all this it?” is a fair question issues, then spend more information in the when the demonstration time finding, interpreting model already. needs speeding up to hold and reporting on what we We just need to your attention. have produced. Much of Schuetz summed up the make it accessible the knowledge needed to customer problem as make sense of it remains more easily “there’s an ambition in the heads of the people about digitalisation, and who did the work. Revizto there’s the reality of no brings multidisciplinary one has time.” Project teams are already models and drawings together with clashbusy. Asking them to work out what an es, issues, responsibilities and the discusAI connection is good for, develop the sions surrounding their resolution. An prompts and check the results, adds assistant can therefore query information work before it removes any. Revizto has about the building alongside the record of been looking at customers’ roles and people trying to deliver it. Model properdaily tasks to establish where an assis- ties alone will not tell you why an issue tant might actually earn its keep. Schuetz has been open for three months. says he hired “three to four times more MCP provides a common way for AI

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applications to call tools and retrieve information. It does not make missing data appear, speed up a slow API or decide what a project manager needs for Monday morning’s meeting. Schuetz’s team drew on practitioner knowledge, including that of Revizto’s chief innovation officer Jason Howden, alongside the RIBA Plan of Work and ISO 19650. They examined the tasks different people do during the day and the information required. A project manager preparing a briefing and a coordinator working through clashes may be looking at the same project, but they have different questions. Revizto encoded this work into reusable Claude skills. These provide instructions and background knowledge to help the assistant select information and interpret what comes back, saving users from having to explain the job every time. Schuetz said the team had written around 40–50 internal skills and used them to explore whether Revizto could support the tasks. “We need to improve the following APIs because we can’t get the answers,” was how he described some of the findings. To put that research to work, Revizto built the Project Intelligence Blueprint, a dashboard running in Claude Cowork. It uses skills to help turn project data into briefings, coordination analysis and proposed actions for different roles. A project manager’s morning briefing gave the team a practical test: could the assistant retrieve the information, make sense of it and return something useful? The Blueprint became both an example customers could adapt and a way for Revizto to test its own APIs. The volume of information was one immediate problem. Schuetz cited a proSeptember / October 2026

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changes reviewed before approval. Access through MCP follows the user’s existing Revizto permissions. The demonstration used sample project data, so it offered no measure of time saved on a live job. What interested me was where this could lead. Revizto already brings together model information and the record of coordination work. That gives it the potential to become the place external agents go to query a project and carry out defined tasks, without repeatedly opening the authoring application. Briefings and issue updates are an immediate use; the larger opportunity is to support more of the automated work firms are beginning to develop themselves.

Too much information ject containing 500,000 issues, where loading everything into memory was impractical. The API needed to provide counts, samples and rules for working with the results. He said the development team had reported changes that made returning issues fifteen times faster. “We’re running this to pressure test our MCP and APIs,” he explained.

From briefing to action Revizto deliberately made the Project Intelligence Blueprint something customers could take and adapt. “Customers should just take it and do whatever they want to do with it,” Schuetz explained. Delivered as a Claude Cowork artefact, the public package includes three skills covering installation, platform knowledge and API knowledge, with views for briefings, project checks, comparisons,

coordination analytics, questions and actions. Firms can alter it to suit their own roles and working practices, using Revizto’s research as a starting point. Schuetz demonstrated a briefing that picked out accumulated critical issues, a low closure rate and an apparent concentration of responsibility on one person. The user could then prepare changes to issue assignments, priorities and deadlines, and approve the updates. This is where the demonstration became more interesting. A report that tells you there is a problem still leaves you with the administration; being able to approve and apply the resulting changes could remove some of that work. The public Blueprint documentation describes this write-back capability. It opens read-only each session, with writing enabled by the user and proposed

Selecting the right information affects both response time and processing cost. “Why do you need the full house and the full 3D model information if you need one room and what’s in there?” Schuetz asked. The Blueprint takes headline totals from Revizto’s counts and uses labelled samples in detailed panels, showing how much of the underlying information has been included. This gives the assistant a manageable selection to work with, alongside the project-wide totals. At the time of our conversation, Revizto was still preparing to expose object properties through MCP. Schuetz said the team was adding filtering at the API level so the assistant could retrieve the information it needed without being overwhelmed by the volume. The current documentation lists scene and model properties as available, with object-level properties accessible when Extended API access is enabled for the project. Images, web-

Revizto versus Autodesk Revizto and Autodesk approach coordination from different directions. Revizto brings multidisciplinary models, drawings, clashes and issue management into one environment. Autodesk combines Navisworks’ desktop model aggregation and clash detection with the shared coordination and construction records in Forma. Its Coordination Issues add-in connects the two, allowing teams to create and respond to issues arising from the desktop clash work. Autodesk has an advantage in

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access to Revit data. Its AEC Data Model API can query elements and properties from supported models hosted in Forma, and search and compare model versions, without running Revit on a desktop. Revizto’s documented Revit publishing workflow still requires the authoring application. Revizto, meanwhile, provides a common coordination environment for models from multiple authoring systems, with the discussions, assignments and decisions attached to their issues. Autodesk also supports multiple formats, but its

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ability to extract granular Revit data is a particular strength. Both are now applying AI to the work surrounding those records. At Autodesk University 2026, Autodesk announced beta Document and Sheets agents for finding information, alongside a beta Submittals Evaluation Agent for comparing submissions against specifications. Revizto’s Blueprint concentrates on project briefings, coordination analysis and approved updates to issues. Its role-based skills give customers a starting point they can modify, while the MCP server lets

them connect compatible assistants of their own choosing. Autodesk’s wider construction platform gives its agents access to tasks beyond Revizto’s coordination focus. Revizto’s proposition is more concentrated: use the information already gathered for coordination, make it accessible to the customer’s AI tools and help act on the findings. The choice will depend partly on where a firm’s project records already sit and how much it wants to build around them. Neither approach removes the need to keep those records current.

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hooks, project and account logs, and model import/export remain outside the MCP server’s documented scope. Other compatible AI clients can connect to the server, while the packaged Blueprint dashboard and its installation workflow are built for Claude Cowork.

Publishing still matters There is a less glamorous dependency underneath all this: publishing the model. Revizto’s documented Revit workflow requires both applications to be installed on a computer, using the plug-in to publish models and sheets. The Scheduler can run publishing tasks out of hours, but the machine doing the work still needs the authoring application installed. An AI querying Revizto sees the model information last published there, making the publishing schedule part of the AI workflow. Revizto currently retains full model versions, something Schuetz wants to improve. “I would like to go way more to delta updates,” he said, capturing what has changed to “compress the amount of information”. He described the volume of data on very large projects, particularly in the Middle East, as pushing the software towards its limits. More granular updates would reduce the amount it has to handle. Autodesk’s AEC Data Model offers a different route to model information, querying supported Revit models held in its cloud without a separate desktop extraction process, as outlined in the box-out below.

Subscriptions, tolls and control I put it to Schuetz that Revizto’s own price increases were becoming a complaint among the design IT directors I speak to. Whatever savings a vendor can demonstrate, its subscription still has to fit within a finite software budget. Autodesk’s increases leave less room for everyone else, and Revizto cannot assume that customers will welcome another charge simply because it has added AI access. Schuetz said the company had examined several pricing models. “We decided to not increase the price, to not introduce tokens, to not introduce a different licence.” Providing the infrastructure had cost more than he initially expected, but Revizto had decided to absorb that expense. Customers would continue to pay for their chosen AI service. “I also don’t want to charge people to get access to their own data,” he added. Access still www.AECmag.com

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needs to be configured: the documentation requires API access and activation of the MCP server app, with Extended API access enabled on projects where object properties are required. Autodesk’s approach to some data services is different. Its AEC Data Model API pricing took effect on 6 October 2026, with monthly usage allowances included in qualifying subscriptions and paid options beyond those allowances. The published AEC rates apply to “Data Out”, where data moves from the AEC Data Model to external data stores. Imports, administration and product-to-product API use within Autodesk are not charged under those rates. The charging applies to qualifying data access, rather than to MCP connections as such. Frequent queries across a portfolio could consume those allowances faster than occasional questions about a single project. Firms building these connections into everyday work will have to account for that usage alongside the cost of their AI service. Schuetz’s commitment to avoiding an additional access toll is welcome, particularly given the pressure on customers’ existing software budgets. It gives firms more room to experiment with automation without having to account for another layer of usage charges.

Putting the ‘I’ to work We spend years producing structured BIM information, then flatten much of it

into drawings for downstream consumption. We are now applying AI to those drawings to recover some of the meaning we discarded. As Schuetz put it, “We have all this information in the model already. We just need to make it accessible more easily.” Revizto’s skills work addresses who needs that information and what they intend to do with it. It also exposes gaps in the software and in the data customers supply. Missing properties and uncertain revisions become difficult to overlook when somebody expects a dependable answer before the next meeting. Revizto has an opportunity to become the place customers turn to for everyday project questions and coordination work. Understanding those jobs, and helping with the administration they generate, gives it something more useful to demonstrate than a conversation with a model. Whether it earns that position will depend on the quality of its answers, the time it saves and whether its commercial promises survive growing use. ■ www.revizto.com

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Software

Qonic edges towards BIM authoring Qonic’s latest updates push its browser-based BIM platform further into authoring, so users can start to build complete models from scratch, writes Martyn Day

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category of specialist work disappears. Get it wrong and the component library becomes another thing to curate, and that is the exact burden Qonic’s platform was built to remove.

and edited together. Split and sliced geometry can be converted straight into an assembly, keeping the relationship between the resulting products intact. Copy and paste has moved into the rightclick menu and select-same works on an Qonic in the real world active selection. None of this makes a World Context is another addition of launch video, but it is the layer that substance. It streams 3D Tiles into the decides whether software survives an model view and drops the project at its eight-hour day. Closing the gap ‘Issues’ has gone from a model-level correct geographic coordinates, so a pro‘Components’ is the headline feature. It posal can be seen against the real terrain, view to a project-level one. Users can see puts doors, windows, furniture, and neighbouring buildings, and infrastruc- and filter everything raised across the MEP terminals into the modelling sys- ture. Sightlines, access routes, and mass- project, including items sitting in models tem. Walls and slabs were already there, ing checks become an orbit of the model, they do not have open. This is more usebut without components there was a not an exercise in assembling a separate ful to a coordination lead running a federated job than to limit to how someone drawing much of a project walls. The 4D anyone could Components’ is the headline feature. It puts doors, planning tools develop before get smaller going back to windows, furniture, and MEP terminals into the changes in the Revit. Qonic says modelling system. Walls and slabs were already there, but same direction, users no longer without components there was a limit to how much of a with a toggle for need external project anyone could develop before going back to Revit unassigned prodcomponent ucts, editable libraries, and that planning delays, the new elements behave as functional, reusable compo- context file by hand. Most of the newer and task sorting and grouping. nents carrying properties rather than platforms are heading this way, treating lumps of geometry. They remain part of building and site as a single environ- Revit and beyond the structured model. ment, and it is one of the few places The feature worth watching, buried in Components such as Windows are where the browser holds a structural interoperability, is Phasing, which now where that gets tested, as they are among advantage over the desktop. exports as a string parameter, which the most relationally awkward objects in a Measurement has been consolidated Qonic says opens the door to pushing building model, tied to sills, reveals, lin- into a single tool. A measure-along edits to that parameter back into Revit. tels, casings, and the thickness of the host option traces a path and returns XYZ Materials also survive a Revit re-export wall. The usual answer has been a library coordinates, signed values, and running and update. This ability to write-back is of heavily constrained families built by totals per axis. Point-to-point measure- what separates a platform that can sit specialists, and occasionally by people ment picks up angles, so angle checks no alongside Revit from one that only works writing scripts. Qonic has gone the other longer need a separate command. downstream of it, and small gains there way, using procedures that update the count for more than any individual modsurrounding context when someone Production tools elling function. The rest of the release is moves, copies, or resizes an opening. The Inspect panel groups identical prod- repair work, covering subtractor creaGet that right at production scale and a ucts, so repeated objects can be reviewed tion, clash calculations for aggregate

onic’s latest updates add native components, streamed site context, rebuilt measurement tools, and a project-wide issues view. The company says this is the point at which a complete building can be modelled without leaving the browser. That is a bigger claim than the feature list initially suggests.

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1 Component library 2 Modelling in context (Cesium) 3 Quality Hub and Natural Language Processing modelling 4 Qonic wall tool

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walls in product snapping, BCF uploads, unit comparison on import, and failure messages that report the underlying cause instead of a generic warning. The release follows Qonic’s addition of Revit 2027 support in July. Set against Snaptrude, Arcol, and Forma, all working the same territory from different directions, Qonic remains the most explicitly construction-facing of them – and its UI looks beautiful. Qonic’s bet on procedures over constrained families is a real strategic difference. Drawings are absent from the release notes but not from the product. Qonic added browser-based floor plans, sections, automatic annotation, and DWG export in April, as seen at NXT BLD (www. tinyurl.com/Qonic-NXT26) Documentation exists. The question is whether it has the depth, control, and reliability that production delivery demands. Components close an obvious hole in the modelling. Qonic’s documentation tools will settle the larger question of whether it replaces an authoring tool or sits beside one. The update video can be seen at www.tinyurl.com/Qonic-0826.

The future

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Qonic is hosting an event at its Ghent HQ on 29 October, where more major features and a glimpse of its future direction are being promised. In the meantime, the developer has just released its October update, which introduces smart snapping to relevant elements (rather than simply the nearest geometry) with an understanding of context, alongside a host of workflow improvements. It’s now possible to cut and paste between projects, create grids, measure the heights of selections, set project units, and manage model tags. The biggest tease has been the demonstration of wall creation, as Qonic finally starts fleshing out more traditional BIM authoring tools. This suggests architectural design capabilities are imminent, having already become something of a darling of construction BIM. Roll on ‘Qonic-con’ at the end of the month. ■ www.qonic.com

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Ten conceptual designs before lunch…

But, two weeks to check and sign the drawings

The largest productivity prize in architecture is the industrial production of drawings that are ready to issue: generated from the model, checked, compared against the last release and fit to be signed. That is where the fee sits, where the delays happen, and where the signatures are. But automating the drawing is the beginning of that prize, not the whole of it, writes Richard Harpham

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udging by the exhibition floors, product launches and conference programs of the past two years, architectural AI has a favourite subject: the beginning of a project. Textto-image concept exploration. Generative massing across a hundred site permutations. Agentic assistants that will interrogate a planning code, sketch a façade, or turn out a morning’s worth of alternatives where a studio used to manage three. Much of it is impressive, and some of it will change how buildings are conceived. Now count the demos about drawing production. The 400-sheet consultantcoordinated set. The dimension strings. The tag that overlaps a door swing on sheet A-204. The thing that gets built from. Not everyone has missed it. At NXT BLD this year, Robert Gräbert of Graebert, Adi Shavit of Swapp and Bill Allen of Chaos shared a panel on drawing automation (www.tinyurl.com/nxtbld-drawings). Three companies from quite different starting points, arriving at the same unglamorous corner of the workflow. A visualisation business, an AI startup and

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a CAD developer do not converge by accident. It is worth asking what they can see that the main stage cannot. What they can see is where the money is.

Follow the fee The AIA’s familiar phase-based rule of thumb allocates roughly 15 per cent of the design fee to schematic design, 20 per cent to design development, 40 per cent to construction documents, 5 per cent to bidding and 20 per cent to construction administration. Proportions vary by project and firm, but the shape does not: construction documents are consistently the largest single phase. The AIA’s own billings research points the same way, finding schematic design and design development together account for only just over a third of billings, while construction documents alone account for around 40 per cent. So, the phase attracting the industry’s AI attention represents a relatively small share of the design fee. The phase attracting comparatively little represents the largest single share and produces the documents on which an enormous volume of

downstream construction activity depends. To be precise rather than sweeping: this is an argument about where the fee sits, not a claim about hours, and where the fee sits is not the same as what it is paid for. Construction-document fees compensate coordination, judgement, revision cycles and the liability an architect carries at signature, not drawing production on its own. Automating the production line does not automate those other components, and there is no reason to expect the fee to shrink in proportion to the drafting hours it removes. Remarkably few practices publish reliable time-by-phase data at all, which is itself part of the problem.

What the documents cost when they are wrong The downstream evidence is stronger than the upstream evidence, and unusually well documented. Navigant Construction Forum’s 2013 study, still the most-cited benchmark in the industry, examined 1,362 projects worldwide containing more than a milwww.AECmag.com

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Event report

lion RFIs. It found an average of 9.9 RFIs for every $1 million of construction value, or roughly 796 per project. Design professionals estimated each RFI took about eight hours to receive, log, review and respond to, at roughly $1,080 in 2013 dollars, nearly $860,000 of professional time per project. The median time from question to answer was 9.7 days, and more than one in five RFIs was never formally answered at all. Smaller projects fare far worse: those between $5 million and $50 million averaged 17.2 RFIs per $1 million, against 1.1 for the largest jobs in the sample. The figures are a decade old and in 2013 dollars, the documented scale of the problem rather than today’s price. How much of that traffic is a documentation failure rather than a genuine design question? Less settled, and worth separating at the outset: plenty of RFIs originate outside the drawings altogether, design uncertainty the model has not yet resolved, contractor preference, coordination changes made after issue, or the commercial tactic of generating a paper trail. Better drawings do not touch that portion of the number. One study of building projects attributed 77 per cent of RFIs to inconsistencies, errors or omissions in documentation, though the sample was small and the equivalent figure for the civil projects examined was 34 per cent. Document quality is clearly a major driver of RFI volume within that subset, but the profession lacks the industrywide dataset it ought to have to say how large the addressable share really is.

The counter-intuitive case for automating the boring work Generative systems are least reliable where judgement is the deliverable. A massing study that is subtly wrong is not a defect, it is a suggestion, and you discard it. The design intelligence remains yours because the machine’s output was never authoritative. Production work is different, and the difference is worth stating carefully. The stakes are unambiguously higher: a misswww.AECmag.com

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ing dimension reaches site in a way a rejected massing option never does. But many production outputs are far more objectively testable. A dimension can be checked against geometry. A missing tag can be detected. An annotation collision can be measured. A changed element can be compared against the last issue. Higher consequences mean verification matters more, but verification is possible, which is not true of most concept-stage output. That reframes what “AI in production” will really look like. Much of the most valuable AI here will not look much like AI at all. It will be hybrid: language models interpreting intent, deterministic engines executing rules, geometry and vision systems spotting conflicts, conventional software verifying the result. The view, sheet, tag and dimension automation of Chaos Glyph is largely rules-and-workflow engineering, with its AI Assistant supplying the natural-language interface; parts of Graebert ARES Neo are deterministic automation rather than anything generative. The point is not to put a language model in charge of the drawing set. It is to use AI where ambiguity helps and automation where certainty matters.

Three bets on where automation belongs The three companies on that NXT BLD panel disagree fundamentally about where drawing automation should live. Chaos Glyph, originally EvolveLAB’s product, now part of Chaos alongside Enscape and V-Ray, puts automation in the seat. It is a Revit plugin that creates views, sheets, dimensions and tags, with tasks combined into reusable bundles that run in a click; a recently added AI assistant accepts plain-language instructions such as dimensioning all interior walls. Commercially it behaves like a plugin, and Chaos publishes the price: a named licence is currently $600 a year. This is automation as personal productivity, bought the way architects have always bought tools, by the desk, adopted bottom-up by whoever is in pain.

Swapp puts it in the firm’s institutional memory. The pitch is an intelligence layer rather than a plugin: a two-week onboarding encodes a practice’s graphic standards, annotation rules and QA logic, after which the system applies them and absorbs the team’s corrections. Swapp says it works inside both Revit and Archicad, that it can automate up to 80 per cent of construction-documentation tasks, and that it has delivered more than 35 million square feet of production-grade CD sets. Those are company figures rather than independent findings, but a customer list including Stantec, HGA, Page and Woolpert suggests meaningful enterprise traction. Most striking is how Swapp frames what firms are buying: succession insurance, capturing how senior staff draft and check so that judgement stays in-house when they leave. That is a sharper articulation of the value than most practices have managed themselves. It does not publish prices, selling instead as an enterprise engagement, which follows logically from a product that begins with a fortnight of onboarding. Graebert ARES Neo, declared interest below, puts it in the pipeline. Graebert describes Neo as a cloud-based automation service that lets Revit users import or create drawings, automatically add dimensions and tags, detect and fix annotation issues, and sync back to Revit and Autodesk Forma; for IFC and Rhino models it generates floor plans, sections and elevations as DWG or PDF. Two things follow from putting the work on a server rather than a desktop. Processing is designed to run in batch across a whole set, or a portfolio of projects, rather than one user’s session at a time. And Graebert intends the automation to run independently of Revit itself, planned rather than shipped, but if delivered it means generating and updating drawings need not consume additional authoring seats, which for a practice paying per licence is a cost question as much as a technical one. Graebert has not published prices but has indicated it will charge by volume of September / October 2026

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drawings rather than by user. These are three theories of the practice, not three versions of a product. Is documentation a personal skill to be accelerated, an institutional standard to be encoded, or an industrial process to be piped? The pricing models give the game away: per seat, per engagement, per volume. How a firm answers says more about the firm than about the software.

Not all saved hours are worth the same

returned to a Part 2 at the start of a work stage, and it is only a genuine saving, rather than a relocation, when the tooling and its exceptions are not quietly consuming it first. This is where change tracking becomes the interesting capability rather than a footnote. Graebert has been developing checking and version-comparison features for Neo, intended to report what has changed between drawing issues, filtered by discipline. If that works as described, it attacks precisely the expensive part of the cycle: it converts a fullset re-check into a delta review, where the reviewer verifies what the system has flagged rather than hunting across sheets for the five per cent that moved. The architect still signs, but signs against an audit trail rather than against their own tired eyes. I would go further: that capability is what I would evaluate these tools on, and it is a reason to buy automation rather than an argument against it. Generation is maturing fast, three credible vendors

and QA rules does not become consistent because it bought AI, it may simply produce inconsistency faster. And be realistic about where saved time goes. As was observed from the NXT BLD stage this year, when AI made concept imagery easier to produce, client expectations expanded to match. The same inflation is coming for documentation, which makes automating the price of staying in the game rather than a guaranteed route to margin.

A last thought to consider Here is the sharper version of my argument. Architects aren’t only pointing AI Developing human IP in your firm in an at the wrong end of the project. Much of age of automation. the industry is solving half the problem For generations, junior architects have and calling it finished. learned how buildings go together partly The measure being used is how much by drawing and detailing them. The procan be generated. The measure that matfession’s traditional apprenticeship was ters is how much is ready to issue, and built around progressively deeper those are not the same number. involvement in drawing, detailing and Consider what happens when automatic coordination, automation risks removing drawing generation succeeds completely. some of that repetitive labour, and some A system produces 400 sheets in an afterof the learning embedded inside it. noon, and your team still checks all 400 That is not a reason to refuse the tools. It against the previous issue, by eye, at deadis a reason to adopt them deliberately, and line. The drawings to treat the design exist; they are not of the apprenticeyet issuable. ship as seriously as Automation will expose the quality of the standards Industrialise only the design of the beneath it: a firm with inconsistent naming, annotation the first half and workflow, because you lengthen the the replacement and QA rules does not become consistent because it queue at the second. will not appear by bought AI, it may simply produce inconsistency faster And consider itself. who does that The demos will checking. Drawing keep being about production is largely junior and mid- already do it well, and a practice still plac- concept design, because concept design weight labour. Verification and sign-off ing dimensions by hand is simply paying photographs well. The returns are in the are not: they are done by project archi- more than it needs to. The harder, more drawing set. tects, associates and principals, because valuable step is proving what changed, to it is their signature and their profession- a standard a signing principal will accept. Disclosure: the author provides commercial adviWhich brings me to a metric the profes- sory services to Graebert on ARES Neo. He has al indemnity on the line. Automation that saves drafting hours saves the sion should start keeping. There is sur- no commercial relationship with Chaos or Swapp. cheapest hours in the practice, in theory. prisingly little published benchmarking In practice that saving is not guaranteed: on how long practices spend checking Sources cut junior production and the freed time major drawing issues. In conversations • AIA phase fee allocation and billings distribution: AIA, Defining the architect’s basic services; AIA Architecture Billings Index does not disappear from the fee, it relo- with large firms, I have repeatedly heard white paper, Designing the Construction Future. cates, to senior staff configuring the soft- periods approaching two weeks from • RFI benchmarks: Navigant Construction Forum, Impact & Control of RFIs on Construction Projects (2013), 1,362 projects, ware, tuning rule sets and standards, and model freeze to signed release, repeated a 1.1m RFIs, 796 per project, 9.9 per $1m, ~8 hours and ~$1,080 resolving the exceptions the system can- dozen or more times across a project. That per RFI, 9.7-day median reply, 21.9% unanswered; 17.2 RFIs not. A practice that trades an associate’s is anecdotal, not industry data, but it per $1m on $5m–$50m projects. afternoon of drafting for a principal’s points at a number every practice ought to • RFIs attributable to documentation issues: Morales et al., Potential Application of BIM in RFI in Building Projects, afternoon of exception management has know and almost none measures. Buildings 12(2), 2022, small sample; use with caution. Call it freeze-to-issue time. How long • Chaos Glyph features and pricing: chaos.com/glyph (accessed not found a cheaper hour; it has found a August 2026). more expensive one wearing the same does it take your firm to get from model • Swapp claims, integrations and customer list: swapp.ai timesheet code. Automation that saves freeze to issued set, and how many times (accessed August 2026), vendor-reported figures. checking hours is a cleaner win: it saves will you do it on this project? If you can- • ARES Neo shipping capabilities: graebert.com/auto-drawings/ ares-neo (accessed August 2026). Change-tracking/versionthe most expensive hours outright and not answer, you cannot evaluate any of comparison, server-side batch processing at set and portfolio does so at the exact point where a delay these tools honestly, because you don’t scale, Revit-independent operation, and volume-based rather than per-seat pricing are all described by Graebert as planned stops a tender package, a trade buyout or know what your bottleneck costs. or in development, confirm status and permission to publish Two cautions. Automation will expose a fabrication release. An hour returned to before press. a principal at 11pm before an issue is sim- the quality of the standards beneath it: a • NXT BLD 2026 drawing automation panel: Robert Gräbert (Graebert), Adi Shavit (SWAPP), Bill Allen (Chaos). ply not the same asset as an hour firm with inconsistent naming, annotation

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