E.R.B. Rail JV PS, the joint venture leading the construction of the Rail Baltica mainline in Latvia, one of the largest infrastructure projects in Europe, has chosen Vektor.io as its digital platform for managing and visualising infrastructure design information.

ERB will used the platform to bring together 2D plans, BIM models, GIS data, and other reference materials spread across many different formats and systems, directly in the browser, accessible both in the office and on site

“We chose Vektor.io because it enables our team to access all design information in one platform, regardless of file format – to view 2D, 3D and survey data together,” said Agnis Mārtiņš Bērziņš, Regional BIM Coordinator of E.R.B. Rail JV PS.

“This helps all team members to work more efficiently by providing access to not only design, point clouds, orthophotos, and public maps with various data, but also to impressive measurement, sectioning, quantifying and analysing tools to ensure rapid and well-informed decision making.”

“We are proud to work alongside E.R.B. Rail on Rail Baltica,” added Teemu Nivell, Chief Commercial Officer, Vektor.io. “What excites us most is how technology supports their teams in daily work – from design reviews to measurements – turning complex data into a practical tool everyone can use.”

E.R.B. Rail JV PS is a partnership between Eiffage Génie Civil SAS (France), Budimex S.A. (Poland), and Rizzani de Eccher S.p.A. (Italy).

The consortium is delivering a high-speed rail corridor that will connect the Baltic States with the broader European network.

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Nemetschek Group – the AECO software developer whose brands include Graphisoft, Vectorworks, Allplan and Bluebeam – and Takenaka, one of Japan’s largest construction companies, have signed a Memorandum of Understanding (MoU) to advance digital transformation and AI-driven solutions in the construction sector.

The MoU initiates a strategic partnership to develop and pilot AI-assisted, cloud-based, and open digital platforms that streamline and enhance collaborative workflows across planning, design, construction, and operation processes.

“This partnership with Takenaka, a true leader with deep expertise in the construction industry, is a pivotal step,” said Marc Nezet, chief strategy officer at the Nemetschek Group. “By combining their extensive, practical know-how with our advanced digital and AI capabilities, we are co-creating a more efficient, sustainable, and data-driven future for the entire AEC/O industry.

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“We believe in empowering our partners and customers to combine human-centric AI innovations with sustainability across the building lifecycle.”

Key areas outlined within the agreement include a commitment to best practice exchange through regular knowledge-sharing sessions, methodologies, and operational insights.

Nemetschek and Takenaka will also focus on joint AI and digital platform innovation, working together to identify, prioritise, and develop cloud-based digital and AI solutions for the AECO sector.

Secure data sharing and validation form another cornerstone of the agreement, with governance models and technical safeguards established to enable data-driven transformation.

Finally, both parties reaffirm their commitment to data protection and compliance, ensuring adherence to privacy, security, and intellectual property standards in line with global best practices.

“This partnership embodies the forward-thinking spirit of our industry,” said Daniel Csillag, CEO of Graphisoft. “By partnering with Takenaka Corporation, we are laying the groundwork for truly collaborative, open, and data-driven workflows that benefit architects, engineers, and contractors worldwide. We are proud to contribute our expertise and technology towards this transformative journey, also building on an existing Enterprise Licensing and Service Agreement between Graphisoft and Takenaka Corporation.”

Nemetschek stated that the MoU serves as a foundation and guiding framework for future joint project-specific agreements. The agreement takes effect immediately and will remain in place for a period of five years.

Main image caption: From left to right: Mr Tetsuo Harada (Executive Managing Officer, Takenaka Corporation), Mr Daniel Csillag (CEO, Graphisoft), Mr Susumu Matsuo (General Manager, Digital Division Head Office, Takenaka Corporation).

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At AU 2025, Autodesk took a significant step forward in its AI journey, extending far beyond the slide-deck ambitions of previous years.

During CEO Andrew Anagnost’s keynote, the company unveiled brand-new AI tools in live demonstrations using pre-beta software. It was a calculated risk — particularly in light of recent high-profile hiccups from Meta — but the reasoning was clear: Autodesk wanted to show it has tangible, functional AI technology and it will be available for customers to try soon.

The headline development is ‘neural CAD’, a completely new category of 3D generative AI foundation models that Autodesk says could automate up to 80–90% of routine design tasks, allowing professionals to focus on creative decisions rather than repetitive work. The naming is very deliberate, as Autodesk tries to differentiate itself from the raft of generic AEC-focused AI tools in development.

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neural CAD AI models will be deeply integrated into BIM workflows through Autodesk Forma, and product design workflows through Autodesk Fusion. They will ‘completely reimagine the traditional software engines that create CAD geometry.’

Autodesk is also making big AI strides in other areas. Autodesk Assistant is evolving beyond its chatbot product support origins into a fully agentic AI assistant that can automate tasks and deliver insights based on natural-language prompts.

Big changes are also afoot in Autodesk’s AEC portfolio – developments that will have a significant impact on the future of Revit.

The big news was the release of Forma Building Design, a brand-new tool for LoD 200 detailed design (learn more in this AEC Magazine article). Autodesk also announced that its existing early-stage planning tool, Autodesk Forma, will be rebranded as Forma Site Design and Revit will gain deeper integration with the Forma industry cloud, becoming Autodesk’s first Connected client.

neural CAD

neural CAD marks a fundamental shift in Autodesk’s core CAD and BIM technology. As Anagnost explained, “The various brains that we’re building will change the way people interact with design systems.”

Unlike general-purpose large language models (LLMs) such as ChatGPT and Claude, or AI image generation models like Stable Diffusion and Nano Banana, neural CAD models are specifically designed for 3D CAD. They are trained on professional design data, enabling them to reason at both a detailed geometry level and at a systems and industrial process level.

neural CAD marks a big leap forward from Project Bernini, which Autodesk demonstrated at AU 2024. Bernini turned a text, sketch or point cloud ‘prompt’ into a simple mesh that was not best suited for further development in CAD. In contrast, neural CAD delivers ‘high quality’ ‘editable’ 3D CAD geometry directly inside Forma or Fusion, just like ChatGPT generates text and Midjourney generates pixels.

Autodesk has so far presented two types of neural CAD models: ‘neural CAD for geometry’, which is being used in Fusion and ‘neural CAD for buildings’, which is being used in Forma.

For Fusion, there are two AI model variants, as Tonya Custis, senior director, AI research, explained, “One of them generates the whole CAD model from a text prompt. It’s really good for more curved surfaces, product use cases. The second one, that’s for more prismatic sort of shapes. We can do text prompts, sketch prompts and also what I call geometric prompts. It’s more of like an auto complete, like you gave it some geometry, you started a thing, and then it will help you continue that design.”

On stage, Mike Haley, senior VP of research, demonstrated how neural CAD for geometry could be used in Fusion to automatically generate multiple iterations of a new product, using the example of a power drill.

“Just enter the prompts or even drawing and let the CAD engines start to produce options for you instantly,” he said. “Because these are first class CAD models, you now have a head start in the creation of any new product.”

It’s important to understand that the AI doesn’t just create dumb 3D geometry – neural CAD also generates the history and sequence of Fusion commands required to create the model. “This means you can make edits as if you modelled it yourself,” he said.

Meanwhile, in the world of BIM, Autodesk is using neural CAD to extend the capabilities of Forma Building Design to generate BIM elements.

The current aim is to enable architects to ‘quickly transition’ between early design concepts and more detailed building layouts and systems with the software ‘autocompleting’ repetitive aspects of the design.

Instead of geometry, ‘neural CAD for buildings’ focuses more on the spatial and physical relationships inherent in buildings as Haley explained. “This foundation model rapidly discovers alignments and common patterns between the different representations and aspects of building systems.

“If I was to change the shape of a building, it can instantly recompute all the internal walls,” he said. “It can instantly recompute all of the columns, the platforms, the cores, the grid lines, everything that makes up the structure of the building. It can help recompute structural drawings.”

At AU, Haley demonstrated ‘Building Layout Explorer’, a new AI-driven feature coming to Forma Building Design. He presented an example of an architect exploring building concepts with a massing model, “As the architect directly manipulates the shape, the neural CAD engine responds to these changes, auto generating floor plan layouts,” he said.

But, as Haley pointed out, for the system to be truly useful the architect needs to have control over what is generated, and therefore be able to lock down certain elements, such as a hallway, or to directly manipulate the shape of the massing model.

“The software can re-compute the locations and sizes of the columns and create an entirely new floor layout, all while honouring the constraints the architect specified,” he said.

This feels like a pivotal moment in Autodesk’s AI journey, as the company moves beyond ambitions and experimentation into production-ready AI that is deeply integrated into its core software

Of course, it’s still very early days for neural CAD and, in Forma, ‘Building Layout Explorer’ is just the beginning.

Haley alluded to expanding to other disciplines within AEC, “Imagine a future where the software generates additional architectural systems like these structural engineering plans or plumbing, HVAC, lighting systems and more.”

In the future, neural CAD in Forma will also be able to handle more complexity, as Custis explains. “People like to go between levels of detail, and generative AI models are great for that because they can translate between each other. It’s a really nice use case, and there will definitely be more levels of detail. We’re currently at LoD 200.”

The training challenge

neural CAD models are trained on the typical patterns of how people design. “They’re learning from 3D design, they’re learning from geometry, they’re learning from shapes that people typically create, components that people typically use, patterns that typically occur in buildings,” said Haley.

In developing these AI models, one of the biggest challenges for Autodesk has been the availability of training data. “We don’t have a whole internet source of data like any text or image models, so we have to sort of amp up the science to make up for that,” explained Custis.

For training, Autodesk uses a combination of synthetic data and customer data. Synthetic data can be generated in an ‘endless number of ways’, said Custis, including a ‘brute force’ approach using generative design or simulation.

Customer data is typically used later-on in the training process. “Our models are trained on all data we have permission to train on,” said Amy Bunszel, EVP, AEC.

But customer data is not always perfect, which is why Autodesk also commissions designers to model things for them, generating what chief scientist Daron Green describes as gold standard data. “We want things that are fully constrained, well annotated to a level that a customer wouldn’t [necessarily] do, because they just need to have the task completed sufficiently for them to be able to build it, not for us to be able to train against,” he said.

Of course, it’s still very early days for neural CAD and Autodesk plans to improve and expand the models, “These are foundation models, so the idea is we train one big model and then we can task adapt it to different use cases using reinforcement learning, fine tuning. There’ll be improved versions of these models, but then we can adapt them to more and more different use cases,” said Custis. In the future, customers will be able to customise the neural CAD foundation models, by tuning them to their organisation’s proprietary data and processes. This could be sandboxed, so no data is incorporated into the global training set unless the customer explicitly allows it.

“Your historical data and processes will be something you can use without having to start from scratch again and again, allowing you to fully harness the value locked away in your historical digital data, creating your own unique advantages through models that embody your secret source or your proprietary methods,” said Haley.

Agentic AI: Autodesk Assistant

When Autodesk first launched Autodesk Assistant, it was little more than a natural language chatbot to help users get support for Autodesk products.

Now it’s evolved into what Autodesk describes as an ‘agentic AI partner’ that can automate repetitive tasks and help ‘optimise decisions in real time’ by combining context with predictive insights.

Autodesk demonstrated how in Revit, Autodesk Assistant could be used to quickly calculate the window to wall ratio on a particular façade, then replace all the windows with larger units. The important thing to note here is that everything is done though natural language prompts, without the need to click through multiple menus and dialogue boxes.

Autodesk Assistant can also help with documentation in Revit, making it easier to use drawing templates, populate title blocks and automatically tag walls, doors and rooms. While this doesn’t yet rival the auto-drawing capabilities of Fusion, when asked about bringing similar functionality to Revit, Bunszel noted, ‘We’re definitely starting to explore how much we can do.’

Autodesk also demonstrated how Autodesk Assistant can be used to automate manual compliance checking in AutoCAD, a capability that could be incredibly useful for many firms.

“You’ll be able to analyse a submission against your drawing standards and get results right away, highlighting violations and layers, lines, text and dimensions,” said Racel Amour, head of generative AI, AEC.

Meanwhile, in Civil 3D it can help ensure civil engineering projects comply with regulations for safety, accessibility and drainage, “Imagine if you could simply ask the Autodesk Assistant to analyse my model and highlight the areas that violate ADA regulations and give me suggestions on how to fix it,” said Amour.

So how does Autodesk ensure that Assistant gives accurate answers? Anagnost explained that it takes into account the context that’s inside the application and the context of work that users do.

“If you just dumped Copilot on top of our stuff, the probability that you’re going to get the right answer is just a probability. We add a layer on top of that that narrows the range of possible answers.”

“We’re building that layer to make sure that the probability of getting what you want isn’t 70%, it’s 99.99 something percent,” he said.

While each Autodesk product will have its own Assistant, the foundation technology has also been built with agent-to-agent communication in mind – the idea being that one Assistant can ‘call’ another Assistant to automate workflows across products and, in some cases, industries.

“It’s designed to do three things: automate the manual, connect the disconnected, and deliver real time insights, freeing your teams to focus on their highest value work,” said CTO, Raji Arasu.

In the context of a large hospital construction project, Arasu demonstrated how a general contractor, manufacturer, architect and cost estimator could collaborate more easily through natural language in Autodesk Assistant. She showed how teams across disciplines could share and sync select data between Revit, Inventor and Power Bi, and manage regulatory requirements more efficiently by automating routine compliance tasks. “In the future, Assistant can continuously check compliance in the background. It can turn compliance into a constant safeguard, rather than just a one-time step process,” she said.

Arasu also showed how Assistant can support IT administration — setting up projects, guiding managers through configuring Single Sign-On (SSO), assigning Revit access to multiple employees, creating a new project in Autodesk Construction Cloud (ACC), and even generating software usage reports with recommendations for optimising licence allocation.

Agent-to-agent communication is being enabled by Model Context Protocol (MCP) servers and Application Programming Interfaces (APIs), including the AEC data model API, that tap into Autodesk’s cloud-based data stores.

APIs will provide the access, while Autodesk MCP servers will orchestrate and enable Assistant to act on that data in real time.

As MCP is an open standard that lets AI agents securely interact with external tools and data, Autodesk will also make its MCP servers available for third-party agents to call.

All of this will naturally lead to an increase in API calls, which were already up 43% year on year even before AI came into the mix. To pay for this Autodesk is introducing a new usage-based pricing model for customers with product subscriptions, as Arasu explains, “You can continue to access these select APIs with generous monthly limits, but when usage goes past those limits, additional charges will apply.”

But this has raised understandable concerns among customers about the future, including potential cost increases and whether these could ultimately limit design iterations.

The human in the loop

Autodesk is designing its AI systems to assist and accelerate the creative process, not replace it. The company stresses that professionals will always make the final decisions, keeping a human firmly in the loop, even in agent-to-agent communications, to ensure accountability and design integrity.

“We are not trying to, nor do we aspire to, create an answer, “says Anagnost. “What we’re aspiring to do is make it easy for the engineer, the architect, the construction professional – reconstruction professional in particular – to evaluate a series of options, make a call, find an option, and ultimately be the arbiter and person responsible for deciding what the actual final answer is.”

AI computation

It’s no secret that AI requires substantial processing power. Autodesk trains all its AI models in the cloud, and while most inferencing — where the model applies its knowledge to generate real-world results — currently happens in the cloud, some of this work will gradually move to local devices.

This approach not only helps reduce costs (since cloud GPU hours are expensive) but also minimises latency when working with locally cached data.

AI research

Autodesk also gave a sneak peek into some of its experimental AI research projects. With Project Forma Sketch, an architect can generate 3D models in Forma by sketching out simple massing designs with a digital pencil and combining that with speech. In this example, the neural CAD foundation model interacts with large language models to interpret the stream of information.

Elsewhere, Amour showed how Pointfuse in Recap Pro is building on its capability to convert point clouds into segmented meshes for model coordination and clash detection in Revit. “We’re launching a new AI powered beta that will recognise objects directly from scans, paving the way for automated extraction, for building retrofits and renovations,” she said.

Autodesk has also been working with global design, engineering, and consultancy firm Arcadis to pilot a new technology that uses AI to see inside walls to make it easier and faster to retrofit existing buildings.

Instead of destructive surveys, where walls are torn down, the AI uses multimodal data – GIS, floor plans, point clouds, Thermal Imaging, and Radio Frequency (RF) scans – to predict hidden elements, such as mechanical systems, insulation, and potential damage.

The AI-assisted future

AU 2025 felt like a pivotal moment in Autodesk’s AI journey. The company is now moving beyond ambitions and experimentation into a phase where AI is becoming deeply integrated into its core software.

With the neural CAD and Autodesk Assistant branded functionality, AI will soon be able to generate fully editable CAD geometry, automate repetitive tasks, and gain ‘actionable insights’ across both AEC and product development workflows.

As Autodesk stresses, this is all being done while keeping humans firmly in the loop, ensuring that professionals remain the final decision-makers and retain accountability for design outcomes.

Importantly, customers do not need to adopt brand new design tools to get onboard with Autodesk AI. While neural CAD is being integrated into Forma and Fusion, users of traditional desktop CAD/BIM tools can still benefit through Autodesk Assistant, which will soon be available in Revit, Civil 3D, AutoCAD, Inventor and others.

With Autodesk Assistant, the ability to optimise and automate workflows using natural-language feels like a powerful proposition, but as the technology evolves, the company faces the challenge of educating users on its capabilities — and its limitations.

Meanwhile, data interoperability remains front and centre, with Autodesk routing everything through the cloud and using MCP servers and APIs to enable cross-product and even cross-discipline workflows.

It’s easy to imagine how agent-to-agent communication might occur within the Autodesk world, but AEC workflows are fragmented, and it remains to be seen how this will play out with third parties.

Of course, as with other major design software providers, fully embracing AI means fully committing to the cloud, which will be a leap of faith for many AEC firms.

From customers we have spoken with there remain genuine concerns about becoming locked into the Autodesk ecosystem, as well as the potential for rising costs, particularly related to increased API usage. ‘Generous monthly limits’ might not seem so generous once the frequency of API calls increase, as it inevitably will in an iterative design process. It would be a real shame if firms end up actively avoiding using these powerful tools because of budgetary constraints.

Above all, AU is sure to have given Autodesk customers a much clearer idea of Autodesk’s long-term vision for AI-assisted design. There’s huge potential for Autodesk Assistant to grow into a true AI agent while neural CAD foundation models will continue to evolve, handling greater complexity, and blending text, speech and sketch inputs to further slash design times.

We’re genuinely excited to see where this goes, especially as Autodesk is so well positioned to apply AI throughout the entire design build process.

Main image: Mike Haley, senior VP of research, presents the AI keynote at Autodesk University 2025  

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London-based KREOD is planning to bring “aerospace-grade precision” to the built environment, with its new KREODx platform which has just launched in beta.

The software harnesses Parasolid from Siemens Digital Industries Software, a geometric modelling kernel that is typically found inside mechanical CAD tools such as Dassault Systèmes Solidworks, Siemens Solid Edge, and Siemens NX.

The software combines Design for Manufacture and Assembly (DfMA) principles with a building-centric approach to Product Lifecycle Management (PLM) — a process commonly used in manufacturing to manage a product’s data, design, and development throughout its entire lifecycle.

KREODx is said to be powered by “Intelligent Automation” with parametric design and engineering workflows that “eliminate errors and accelerate delivery”.

The software offers full support for Bill of Materials (BoM) to deliver what the company describes as a single source of truth for costs, materials, and procurement, giving transparency from model to assembly.

According to the company, KREODx is also aligned with the circular economy, extending building lifespans, reducing waste, and enabling re-use and adaptability over time.

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• Cover story ‘Contract killers’ – most architects overlook software small print, but today’s EULAs are redefining ownership, data rights and AI use — shifting power from users to vendors.
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• From pixels to prompts: How Chaos is blending AI with traditional viz, rethinking how architects explore, present and refine ideas
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• Autodesk shows its AI hand: Autodesk has presented live, production-ready tools, giving customers a clear view of how AI could soon reshape workflows
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• Autodesk Forma is finally expanding beyond its early-stage design roots with a brand-new product – Forma Building Design –  focused on detailed design

It’s available to view now, free, along with all our back issues.

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Allplan’s new 2026 product lineup — developed for architects, engineers, detailers, fabricators, and construction professionals — puts a strong emphasis on automation, collaboration, and sustainability.

To expand its sustainable design capabilities, the new release features an integration with Preoptima Concept, a third party tool for whole-life carbon assessments (WLCAs)

This workflow enables architects and engineers to export conceptual mass models directly from Allplan for early-stage carbon assessment, well before a detailed BIM model is required. In addition to embodied carbon, users can assess operational and whole-life carbon, compare multiple design variants, and make more informed design decisions

With the introduction of GeoPackage DataExchange, urban planners and designers can now integrate GIS data across all EU countries and beyond. According to Allplan, providing access to accurate site context supports better decisions on zoning, infrastructure, and environmental impact.

A new AI Assistant, available through the Connect platform, aims to improve planning efficiency and decision making by providing guidance on Allplan workflows, AEC standards, and best practices, while offering smart suggestions for tasks ranging from construction standards to coding support.

There is also a new free tool, the Allplan Model Viewer, which makes browser-based IFC visualization available to all project stakeholders.

XRef Manager delivers a unified overview and simplifies data management. Cloud Reference, a new IFC XRef type, allows direct insertion of IFC files into Allplan via BIMPLUS, enhancing information sharing within a common data environment.

Allplan 2026 also offers support for IDS-based attribute import, helping ensure BIM models include all the necessary data for compliance, together with enhanced IFC4 import capabilities for smoother data exchange. New interfaces in BIMPLUS for collaboration include IFC 4.3 import and extended export capabilities for GLB format for better visualisation.
Elsewhere, an extension to the Allplan Bluebeam connection enables users to more easily bring documents from the Bluebeam Studio Project environment into their Allplan workflows.

For architects, new tools make it easier to design freeform staircases, while new multilayer support for slabs simplifies the modelling of complex assemblies such as foundations and terrain layers.

BIM-compliant room and finish detailing have been improved with multilayered finishes, rule-based distribution, advanced filtering, and flexible layer management. With custom component priority-based connections, designers can now model complex shapes more accurately while automated interactions reduce manual effort and minimise errors. According to Allplan, these features simplify the creation of precise 2D and 3D representations, reduce errors, and accelerate updates — delivering consistent, detailed results from early design through to sustainable, ready-to-build execution

For infrastructure design, Allplan 2026 introduces a new parametric tunnel modelling approach which integrates alignment, cross-section definitions, and construction methodology, to provide engineers and detailers with ‘precise and efficient’ modelling capabilities, while ‘significantly improving performance’.

There’s also advanced 3D layer management for complex civil projects, and a newly unified Digital Terrain Modeler that is said to enable accurate and detailed terrain workflows. Elsewhere, parametric road intersection modelling has been expanded to enable more realistic intersection models, with reduced manual rework.

For structural analysis and design, it’s now easier to import Structural Analysis Format (SAF) files thanks to a direct import into BIMPLUS, which ‘instantly generate’ a 3D analytical model for visualisation and interrogation. Users can also now ‘seamlessly transfer’ their 3D models to the BIM-Connector making it easier to work across Allplan solutions.

A new mobile loads functionality enables engineers to ‘quickly and automatically’ identify and analyse scenarios, whilst new footfall analysis supports evaluation of how structures respond to human-induced vibrations. Finally, newly amalgamated modules for component-based analysis and design include an expanded unified foundations module.

Structural reinforcement detailing has also be improved with enhanced automation of column reinforcement design. According to the developers, this makes it easier, faster, and more reliable to transfer reinforcement layouts from analysis and design.
Automated wall reinforcement workflows are also enhanced through single-click horizontal or vertical reinforcement priorities and toggling reinforcement types on or off.

Precision for complex circular and spiral structures has been enhanced through updated circular reinforcement capabilities and a new reinforcement to field workflow. Detailers and rebar fabricators can now place reinforcement directly in isometric views, benefit from improved attribute management, and effectively handle reinforcement collisions in a fully digital drawing-less process.

For prefabrication and construction, by uniting design and fabrication in a common workflow, Allplan 2026 enables precast fabricators to benefit from what is describes as consistent outputs with reduced manual work and errors. Rule-based dimensioning automates the generation of production-ready drawings, ensuring every precast element receives the exact dimensions required according to customizable rules and standards. New automated dimensioning for local views enables accurate representation, precision, and consistency despite modifications, whilst customizable quantity takeoff enables users to export Bills of Materials with the data needed for ERP systems or clients.

For steel detailers and fabricators, Allplan’s dedicated structural steel solution for 2026 includes new functionality for creating custom profiles, enabling users to handle specialty detailing and added intelligence for custom connections through constructability checks. Design and detailing possibilities are expanded with support for new material types.

Finally, some significant groundworks improvements in Allplan 2026 are designed to simplify complex construction model management, reduce errors, speed up project delivery, and ensure reliable data handover to construction teams.

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Higharc is a cloud-based service for US housebuilders of timber-frame buildings, aimed at a market of users more likely to use AutoCAD and dumb 2D sketches than BIM.

Having a single focus on a specific type of building and process has enabled the development team to highly automate modelling, drawings, QA, costings and many other parts of the design process. While this may not be aimed at the type of buildings you create, it’s well worth looking at what this expert BIM system can do.

Higharc possesses a wealth of industry knowledge and has already secured significant financial backing, having raised $25 million in Series A funding and later $53 million in Series B funding. The leadership team contains veterans from relevant technology fields, including CEO Marc Minor, who came from the 3D printing world.

There are several former employees of Autodesk. CTO Peter Boyer is an ex-Autodesker who was a founding member of Dynamo, and Michael Bergin, VP of Product, was a research lead for Autodesk’s Industry AEC team. Bergin previously worked on Dreamcatcher, Autodesk’s AI/ML design system for manufacturing design, and his motivation stemmed from recognising the broken system of manual architecture design.

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This year we are starting to see some of the AI work that Bergin has been working on for Higharc users. In April 2025, Bergin released a video demonstrating a very cool use of AI – a new Generative AI capability specifically designed for on-boarding designs to its cloud-based BIM platform. This AI accelerates the conceptual phase by converting a 2D hand sketch directly into a functional 3D BIM model (AEC Magazine covered it here.

Available in beta, the software used visual intelligence to analyse architectural features contained in a plan view sketch. As most people in US residential are not BIM experts and have little CAD knowledge, driving a modelling system from hand drawn sketches bypasses the need to learn complex CAD.

The process involves importing the sketch, which is then analysed to automatically detect room boundaries and wall types. The AI identifies and tags spaces, even handwritten notes denoting room allocation types (like kitchen or bedroom). The “magic” of the process is that it is not simply form finding; it involves mapping the rooms to specific data types within Higharc’s system of defined spaces.

Since Higharc is a data-first BIM system built from the ground up, the AI transforms the sketch directly into this organised data model. In a matter of minutes, the software generates the correlated 3D elements identified in the drawing, including walls, doors, windows, and fixtures.

Expanded AI

In September 2025 Bergin posted a new video to Linkedin showing his latest beta developments in applying AI to the Higharc modelling process.

Moving on from converting sketches to models, Bergin is aiming to take the process from sketch to completed model, with drawings and AI renderings. And ultimately to be able to talk to the model, to get it to update design features.

Bergin explained what the video showed, “Powered by our building design agent, a one-shot prompt turns a floor plan hand sketch into an enriched Higharc model. As with any Higharc model, once translated we have real-time dimensioned plans, building estimates, sales experiences, marketing materials, and more.

“Most importantly, for the first time, we’re opening the door to a research preview-DreamGraph, our first step toward enabling people who are not Pro Homebuilders to experience Higharc firsthand.”

The video kicks off with an imported 2D drawing and on the side menu, the user enters “Analyse this floor plan image and translate to Studio” (studio home design being the modelling tool of Higharc).

The AI replies “Great! I’ll begin analysing your floor plan image and translating it into a Studio home design. This process involves several steps: creating the underlay, detecting and scaling the floor plan, generating rooms, identifying doors and windows, and finalising the layout. Let’s get started.”

We are entering a different phase in design tool evolution. We will start talking to and working with AI from concept to document delivery

The system can then be seen running though routines on the screen. The AI scales the image which is used as an underlay to extract the room outlines. It then detects the room boundaries and converts them into Studio rooms, matching the original floor plan. Doors, windows, and other entities were identified and placed and the layout was analysed and refined to ensure logical room types and adjacencies.

Blocks and roofs were generated for a complete, buildable home structure. All this in a matter of seconds, and you can even look at the structural timber frame for the roof that was never drawn or designed. It’s all quite gobsmacking.

This then initiates the automated documentation capabilities of Higharc, delivering architectural plans, and 3D views and renders. This is the first demonstration of the automation of sketch to model to drawings.

To create a BIM model and all associated documentation, with costings and Bill of Materials, all you need to be able to do is sketch; it’s really quite amazing.

Bergin then posted a subsequent but short video, demonstrating editing capabilities. With the completed model he typed into the native language interface ‘bring out porch 180 inches deep’ and Higharc paused, identified that the existing porch was 96 inches deep and then extended out that part of the model by 84 inches, while maintaining the original porch width.

Expert BIM systems

Higharc is the perfect example of what a BIM 2.0 system can do. The only drawback is that it’s an expert system dedicated to a very niche market. By designing a BIM system to operate within the constraints of a single building type, the team has been able to drill incredibly deeply into the granularity of the construction type, enabling a wealth of riches in terms of data out and automation in modelling, drawing, costings etc. Every house is a variation on a theme and a reconfiguration of the granular entities that make up a US timber frame house.

While in the future the team could expand out to cover other building types and construction methodologies, each building would take immense focus and work to repeat Higharc for concrete offices or modular hotels etc.

In musings with Greg Schleusner, principal and director of design technology at HOK, we have discussed if expert BIM systems are the way forward, as opposed to generic systems, which even most of the BIM 2.0 players are creating, following the Revit replacement route.

HOK has many hotel and labs design jobs, so should there be bespoke BIM systems which cater for these building design types, as opposed to having a generic tool and developing your own internal layers to try and create a customised system? There will always be a problem when the software is created by programmers who have never worked in a design firm and the designers are in the practices knowing all the problems but not writing the software.

The jury is still out on this. But what might change is the impact of AI on coding, applications on demand. Firms may be able to describe a building type with all its nuances and get an automated and programmatic response.

Programs like Hypar could well sit in this space as they are BIM 2.0 and potentially flexible to define expert systems.

Conclusion

We are entering a different phase in design tool evolution. We will start talking to and working with AI from concept to document delivery. This kind of interface is coming to generic BIM tools as well as these powerful expert systems. But due to their intrinsic knowledge of a design type, it’s easier for AI to deliver deep productivity saving results.

With the AI-powered conceptual technology in Forma demonstrated at Autodesk University, Snaptrude’s AI launch and technology like Skema, which can take massing models and replace low level of detail models with high level of detail, productivity savings are coming – and for relatively common building types the level of automation will get quite frightening, quite quickly.

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Trimble has built a new suite of collaboration tools directly into the heart of SketchUp for Desktop, alongside improvements to documentation, site context, and visualisation.

The latest release of the popular push/pull 3D modelling software introduces private sharing control, in-app commenting, and real-time viewing, allowing designers to collect feedback from clients and stakeholders without leaving the SketchUp environment.

“Great designs are shaped by conversation, iteration and shared insight,” said Sandra Winstead, senior director of product management, architecture and design at Trimble. “Rather than jumping between email threads or third-party tools to hold conversations, collaborate and make design decisions, we’ve built collaboration directly into SketchUp.”

With these new tools, designers can securely share models with selected stakeholders, controlling who can view and comment. Feedback is attached directly to 3D geometry, ensuring comments are linked to the right part of the model.

All collaborators see updates instantly, creating what Trimble describes as a shared space for real-time design conversations. Cursor and camera tracking features also allow clients and colleagues to follow along during live presentations.

Elsewhere, SketchUp now includes professional 2D drafting tools in LayOut, the companion application used for presentations and documentation.
According to Trimble, users gain access to more intuitive and precise drawing features for common documentation tasks, along with new scrapbooks offering standard architectural graphics such as doors and windows for scaled 2D composition.

An enhanced DWG export workflow helps ensure that SketchUp geometry and Tags are accurately preserved when transferring designs from 3D SketchUp into 2D CAD or BIM tools.

Trimble has also upgraded Scan Essentials, the SketchUp plug-in for turning point cloud data into 3D models. The latest release makes it easier to incorporate existing buildings into terrain as pre-built 3D geometry, supporting more accurate visualisation, climate analysis, and site planning.

SketchUp’s visualisation capabilities have been further refined, offering greater stylistic control and a broader set of rendering options, including Color Ambient Occlusion, Ambient Occlusion Scale Multiplier, and Invert Roughness.

Finally, for AI-assisted rendering, a new Diffusion Labs update delivers higher-fidelity imagery and greater creative control over AI-generated imagery.

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Snaptrude has announced that the next major release of its collaborative building design platform will bring AI directly into the early stages of design.

With Snaptrude’s ‘AI stack’ users can simply describe a building type, the site, and the intended use, while the software automatically generates a layout with the right spaces, providing an ‘intelligent starting point’ for manual design.

Snaptrude also includes a new built-in AI research capability, designed to help architects save hours by not having to dig through codes, guidelines, and precedent studies before they can start shaping a design. Snaptrude’s AI can analyse building codes and ADA requirements, benchmark space standards and costs.

Meanwhile Snaptrude has enhanced its ‘AI-powered inspiration tools’ – in other words, its AI renderers. Users now have a wider choice of AI models, including Nano-Banana for ‘super-fast image generation for quick iterations’ and Veo 3 for ‘smooth & realistic’ video generation.

For more on Snaptrude AI see our in-depth article, which include an interview with Snaptrude CEO, Altaf Ganihar

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For those of us who operate in the built environment industry, our ability to catalyse change and to drive innovation is vital to our roles as future makers. As problem solvers, tool developers, technology wizards and tinkerers, it can feel natural to jump straight into solutions.

But what if we don’t quite know for what reason we should be developing a solution? It can be difficult to pluck from thin air innovations and new ways of doing things. Divine inspiration is often not the optimum route to a great idea or product. Instead, new ideas are often best identified through more general exploration of the broader domain that they occupy. At Remap, we’ve run numerous hackathons and foresight workshops with our clients, with the aim of teasing out such ideas. Understanding challenges in our industry, working through what might be creating them and then investigating emerging technologies for a response can result in great solutions

Critique culture

In early 2024, Ben Porter and I (Jack Stewart) co-founded Remap off the back of 10-plus years leading digital design at Hawkins\ Brown. As architects at Hawkins\Brown, we would design through a process of inquisition, participating in design charettes and the deeply ingrained critique culture of the profession.

Here, we tested techniques for rapid design exploration and evaluation. Collaborative design charettes are where a team of designers, community members and other stakeholders work together to develop a vision or design for a project. Critiques, meanwhile, provide feedback on how well design meets both user needs and client objectives. Generally, this approach focuses on the ‘why’ and the ‘what’ of design.

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But Ben and I were often curious about the processes that we undertake to design, as well as the technologies that we use to do it. Sometimes, it would feel as though there was a disconnect between designers and the tools that we use to do our work. In other words, it felt as if we, and our colleagues, were sometimes shackled by the design tools that we were using and at the mercy of those companies that build and sell them.

As we started to become more proficient in software development, we became aware of how good the tech industry is at testing, procuring and building new tools. At the most extreme end of the scale, open source culture in tech sees enthusiasts contributing to the improvement of software and platforms almost as a hobby. And for focused efforts, they typically run the equivalent of design charrettes and critiques, for ideas, process and functionality, in the form of hackathons.

As British architect Cedric Price said, ‘If technology is the answer, then what is the question?’ In a hackathon environment, we can discover what problems we need to solve and then rapidly prototype solutions.

Hackathons and heroes

This piqued our interest and, during the 2022 London Festival of Architecture, we hosted a hackathon alongside Here East, Hawkins\Brown, and Wikihouse, bringing together a range of creative minds to explore new ideas for the Wikihouse system. The challenge was simple: ‘Build X, using Wikihouse, for the purpose of Y.’

In this scenario, X is the tool. It might be a digital tool, a physical tool, a building component, strategy or system. Wikihouse, meanwhile, is the framework – the modular system that this tool will work alongside and facilitate. And Y is the purpose or goal.

One team, with the purpose of ‘reducing waste’, developed a tool to turn plywood offcuts into furniture. Another, targeting ‘structural improvement’, proposed a new beam solution for spans wider than were previously possible. Finally, a team addressing ‘cost certainty’ built a handy API for instant costing of an itemised Wikihouse.

It was a super-successful event, with several ideas finding their way into the future Wikihouse development pipeline.

In 2023, while the seeds of Remap were being sown, we listened to a podcast by the Y Combinator, called ‘How to start a start-up’.

In the podcast, Wufoo founder Kevin Hale introduced his ‘King for a Day’ initiative, born out of frustration that so many great hackathon ideas never make it to production.

Often during hackathons, users are working hard on ideas about which, in reality, they are only semi-passionate. In King for a Day, one staff member is randomly chosen, and, for one day only, an idea about which they are truly enthusiastic takes centre stage.

We brought this to Hawkins\Brown in 2023, launching our first ‘Hero for a Day’. This was a callout for practice-wide suggestions to improve practice, design or delivery. Over 50 ideas poured in. From these ideas, ‘heroes’ were chosen and a practice-wide Digital Design Network was assembled to tackle the winning challenges.

In this process, we start by understanding the hero’s idea and then break the problem down into simple, testable steps using pseudocode. We focus on practical solutions, prioritising functionality over appearance, and on what we can realistically accomplish in a single, energised day.

Later in 2023, as part of an automation-focused callout, we requested pain points that could benefit from technology to either improve quality of output or make processes more efficient.

One hero suspected that modelling ceiling setting-out tiles could be automated.

During the session, the team developed a Grasshopper script to automatically generate layouts responding to the ceiling perimeter and structural penetrations. This could then be developed as a Revit add-in leveraged by Rhino.Inside.Revit.

The second hero, frustrated with the conversion of design documents from A3 to 16:9, led a team to develop an automated conversion process. During the session, the team documented how this process worked auto-manually, using InDesign buttons, then daisy-chained the functions together using Javascript.

The third hero, in response to ARB and RIBA requirements to document CPD activities, developed an app to list, calendarise and track activities and attendance. This team’s app allowed users to easily download attendance records and access linked recordings and presentations.

We noticed that most pain points submitted were typically associated with automation, often broad in nature and had a practice-wide impact. To spark more project-specific innovation, we launched a design-focused Hero for a Day in 2024.

One highlight was the Arch-revival project, a computationally designed stone pavilion for Clerkenwell Design Week. Project leads teamed up with the Digital Design Network to create a design engine that automated brick arch layouts and enabled rapid exploration of variations in form and pattern.

Using Grasshopper, the team adjusted parameters such as brick size, mortar joints and arch shape, while attractor controls generated unique clustering effects. Integrating Rhino.Inside.Revit streamlined the workflow, enabling design changes to instantly update elevations and schedules, eliminating tedious redraws.

The result? A more complex, visually stunning pavilion that remained practical to build thanks to digital design tools. The final structure was a stand-out pavilion at CDW, and a testament to the power of computational design in real-world fabrication.

Hacking the future

These successes have given us great momentum. We have since run a forecasting workshop at BILT and hackathons at architecture firms Donald Insall Associates and Scott Brownrigg, with further events organised for later in 2025. These organisations are enthused about how techniques such as hackathons can help firms tap into the creativity and intelligence of their employees and use technology to build their own solutions to problems.

We are firm believers that to have ultimate design agency we need to be able to create & edit our own tools. That is really important to create great buildings.

Let’s embrace this challenge with the creativity, courage, and conviction it demands. After all, the future is not something that happens to us; it’s something we create. Let’s build it.

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