A.Engineer, a new agentic AI platform designed to help civil and structural engineers automate calculations and reporting, has launched in Europe.

Developed by Tyréns NEXT, the innovation arm of engineering consultancy Tyréns Group, A.Engineer combines engineering data, calculation tools, and report generation into a single “intelligent workspace”.

The platform is designed to simplify workflows, allowing engineers to spend less time on manual tasks and more time on creative design, instructions, and quality assurance.

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According to the developers, A.Engineer is built the principle that engineering is empowered by AI yet always verified by a professional user.

The platform provides a full audit trail for every step the AI takes. Engineers can see all data inputs, outputs, underlying code, and the reasoning behind each decision. There are no outputs without verification.

A.Engineer includes an Agentic Calculation Tool Builder, where engineers can upload their own data, Excel tools, or connect to legacy systems, and the system then generates the necessary calculation tools — including both the code and the user interface — “in minutes”.

Meanwhile, an Agentic Report Builder connects data and verified calculations to “automatically build” professional reports with tables, graphs, summaries, and visualisations.

The platform can integrate (via MCP connection) with established engineering tools such as Revit, Sparkel, ETABS, SAP2000, and Strusoft.

According to Richard Parker, senior structural engineer at AKT II and product lead for A.Engineer, early users are already seeing major efficiency gains, “Our research shows that 40–80% of engineering work is still manual,” he said. “With A.Engineer, we can automate over half of those manual tasks. A calculation report that might have taken half a day can now be done in under an hour. Engineers working side by side with A.Engineer deliver world‑class results in record time.”

Europe, the home market of the Tyréns Group, will be the first region to gain access to A.Engineer. A global rollout of A.Engineer is scheduled for Q1/2026.

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Trimble has launched a new “Open and extensible” agentic AI platform, a collection of core services, security frameworks, and tools allow Trimble to build and deploy agentic AI systems.

The company’s vision is to enable partners and customers develop AI agents and multi-agent workflows across Trimble’s suite of construction solutions. Trimble is currently piloting the platform, Trimble Agent Studio, with select customers.

“As agentic AI use cases multiply, there is a growing need for common infrastructure that allows creators to rapidly and responsibly develop, deploy, monitor, and maintain high-value AI agents at scale,” said Mark Schwartz, senior vice president of AECO software at Trimble.

“We see the platform as the engine that will help Trimble, its partners, and its customers extract more value from both our solutions and their data.”

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According to Trimble, its agentic AI platform and other AI capabilities are currently being used to help users learn and navigate Trimble software more efficiently. AI is also being used to eliminate many of the manual steps traditionally required to model from scratch, allowing users to generate 3D objects simply by describing what they want to create.

Trimble’s AI can also convert voice memos into field documents, capture status updates from crews, and reduce the time teams spend in front of computer screens back at the office. Beyond design, Trimble’s AI accelerates access to data and streamlines asset maintenance and permitting workflows.

“We are building an industry ecosystem aimed at breaking down data silos and empowering our customers to make smarter decisions, collaborate effectively and work faster,” said Rob Painter, Trimble CEO.

“By embedding AI into our solutions and enabling improved data flow, we’re taking the next steps towards unlocking the power of connected data.”

Trimble is making several AI tools available through Trimble Labs (Labs), a pre-release, early engagement program that enables customers to test new features and provide user feedback.

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Chaos has released Corona 14, the latest version of its photorealistic architectural rendering engine for 3ds Max and Cinema 4D. New features include AI-assisted creation tools, support for Gaussian Splats, procedural material generation, and new environmental effects.

In Corona 14, Gaussian Splats enables visualisers to “rapidly create” 3D scenes by placing buildings in a real world context, and rendering complex 3D environments with accurate reflections and refractions.

Gaussian Splats, which use AI to create a rich 3D scene from a series of photos or videos, are said to yield smoother surfaces, richer volumetric detail and a more natural sense of depth for designers looking to bring real-life environments and objects into their work.

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Corona 14 also provides access to range of AI tools, which can be enabled/disabled so creatives can decide when to apply them or firms need to follow strict AI policies and adhere to client requirements.

The Chaos AI Material Generator allows creators to upload a photo of a real-world surface and then turn the image into a tileable, render-ready PBR, including all necessary maps, in a few clicks. According to Chaos, it’s an ideal solution for secondary materials that don’t require art direction ― but without loss of realism.

Meanwhile, the Chaos AI Image Enhancer is designed to elevate the realism, texture, and detail of supporting elements — such as foliage, people, or terrain — without altering the core design.

Advanced controls allow users to adjust the appearance of people assets and refine vegetation for precision and consistency. Corona 14 can also enhance AI creativity with the power to send LightMix results directly to the AI Image Enhancer to explore multiple lighting scenarios or fine-tune mood.

AI Upscaler is designed to turn low-resolution drafts or renders into high-quality, presentation-ready visual. According to Chaos, this can save hours of rendering time while still delivering crisp, photoreal results.

Elsewhere, a new Night Sky feature allows designers to add realistic moonlight, stars, and the galactic backdrop of the Milky Way without having to rely on HDRIs.

Finally, a new Fabric Material feature creates fabrics with “true-to-life” woven detail, with full control over the weave or threads — including opacity, bump, displacement, and more.

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Trimble has announced ProjectSight 360 Capture a new tool designed to give contractors better visibility into site progress by applying AI to 360-degree images, captured while walking the construction site.

ProjectSight 360 Capture, which is built into Trimble’s ProjectSight project management software, enables construction teams to conduct virtual jobsite walkthroughs, track progress and resolve issues collaboratively.

A cloud-based AI algorithm automatically processes the 360-degree images, identifies key locations, and links them to project drawings to, enabling comparison of as-built conditions over time or against the design. AI-powered privacy filtering also blurs faces on the jobsite, protecting individual privacy.

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Project managers can connect these images directly to workflows — such as change orders or RFIs — by embedding captures within requests. Through Trimble Connect’s common data environment, all imagery is shared and accessed in one centralised location.

“Traditional methods of capturing and communicating project status are typically time consuming, complex and incomplete, making it difficult for contractors to collaborate, quickly correct problems and keep projects on budget and on schedule,” said Lawrence Smith, vice president and general manager of construction management solutions at Trimble. “Directly pairing critical project management tasks with 360-degree image captures gives users a clear picture of actual conditions and status on job sites, helping turn data into effective decision making.

“ProjectSight 360 Capture makes real-world data easy to capture and use, giving project managers critical insights through intuitive visualisation and navigation,” added Smith. “By streamlining documentation, tracking changes over time, and simplifying issue management across the Trimble ecosystem, it gives project managers a reliable, real-time view of job site conditions regardless of their location.”

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Tektome has unveiled KnowledgeBuilder, an AI-powered platform designed to automatically organise massive volumes of siloed project documents to help AEC teams make smarter decisions while avoiding repeated mistakes.

The software, already piloted by Takenaka Corporation, one of Japan’s largest construction firms, analyses and extracts key content from scattered files — including drawings, reports, photos, and handwritten notes on marked up PDFs — and consolidates the data into a central, structured, and “instantly searchable” knowledge base.

The platform enables architects and engineers to ask questions in plain language and quickly see how similar issues were handled in past projects, eliminating the need to “reinvent the wheel.”

According to the company, even non-IT staff can configure what to pull from drawings, proposals, photos or meeting minutes without coding or complex setup.

KnowledgeBuilder works across PDFs, CAD files (DWG and DXF), scanned images, handwritten markups, and more, with support for BIM files (RVT and IFC) coming soon.

Its search functionality includes two modes: combined filtering across project attributes, file-level attributes, and keywords for precise narrowing, and a semantic keyword search that understands context and synonyms, and highlights matches inside documents and drawings.

According to Tektome, this natural query ability helps teams retrieve critical insights on demand, greatly improving decision-making speed and confidence.

For its KnowledgeBuilder implementation, Takenaka Corporation established multiple working groups. On-site team members took the lead in verifying how as-built drawing data could be structured and after a three-month pilot programme, were able to set up their own custom extraction criteria tailored to their needs. This allowed them to quickly retrieve practical information such as past as-built drawings that matched specific project requirements, reference examples from similar projects, and summaries of relevant numerical data from previous designs.

Takenaka is now expanding the system by importing a larger volume of past as-built drawings and further enhancing search options and usability.

“The shift from manually searching hundreds of thousands of pages of drawings to instant access through natural language has revived veteran design expertise as actionable knowledge, allowing designers to focus on more creative tasks,” said Mr. Takaoka of the Structural Department, Design Division at Takenaka’s Tokyo headquarters.

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ProjectFiles from Part3 is a new construction drawing and documentation management system for architects designed to help ensure the right drawings are always accessible on site, in real time, to everyone that needs them.

According to the company, unlike other tools that were built for contractors and retrofitted for everyone else, ProjectFiles was designed specifically with architects in mind.

ProjectFiles is a key element of Part3’s broader construction administration platform, and also connects drawings to the day-to-day management of submittals, RFIs, change documents, instructions, and field reports.

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Automatic version tracking helps ensures the entire team is working from the most up-to-date drawings and documents. According to Part3, it’s designed to overcome problems such as walking onto site and finding contractors working from outdated drawings, or wasting time hunting through folders trying to find the current structural set before an RFI deadline.

The software also features AI-assisted drawing detection, where files are automatically tagged with the correct drawing numbers, titles, and disciplines.

Meanwhile, learn more about Part3’s AI capabilities, along with tonnes of other AI-powered tools, in AEC Magazine’s AI Spotlight Drectory

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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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CMiC has launched Nexus, a construction ERP (Enterprise Resource Planning) platform powered by more than 25 intelligent AI agents. According to the company, Nexus redefines how construction teams interact with data, automate workflows, and make critical business decisions — enabling them to focus on high-value strategic work rather than repetitive processes.

“Our AI-powered features offer users advanced data visualization capabilities, business intelligence tools, and the ability to leverage natural language to optimise key business functions,” says Gord Rawlins, president & CEO, CMiC.

For project management, AI automates drawing uploads by extracting sheet numbers and titles, organises spec books into trade sections with CSI codes, and pre-fills submittal records to accelerate log creation.

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The Project Pulse Dashboard uses sentiment analysis on daily journal entries to detect emerging issues like delays and safety concerns before they impact project outcomes.

The Financials module enables users to create Balance Sheets, Income Statements, and other financial documents using natural language which, according to the company, dramatically reduces preparation time from hours to minutes.

CMiC’s Analytics capabilities are said to democratise data access as team members are able to ask questions and receive instant insights, regardless of technical expertise. On top of that, agent-driven automation deploys over 25 AI agents across project management and financials to handle routine tasks, from master cost code maintenance to bank reconciliation and financial impact analysis.

In addition to its AI capabilities, Nexus features a modern user interface built on Material 3 design principles, delivering what CMiC descrivbes as enhanced visual clarity, improved readability, and faster information processing.

“NEXUS represents a fundamental shift in how construction teams interact with their ERP. From agentic workflows that handle complex tasks autonomously, to ‘AL’ delivering instant insights, to AI woven directly into the tools you use every day — it all comes together in a modern, unified experience. This is construction technology reimagined for the AI era,” said Steve Cangiano, Chief Product Officer at CMiC.

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Construction technology company mbue has launched a new platform designed to automatically generate “fully compliant” product data submittals, helping subcontractors reduce weeks of manual work to just minutes.

mbue Submittals uses a proprietary AI model and computer vision to analyze drawings and specifications — including electrical schedules — and automatically extracts the required product information. The platform can generate submittals for light fixtures, switchgear, panels, devices, and other materials, streamlining a traditionally time-consuming and error-prone process.

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The company anticipates the new platform will drive productivity by over 30% through reduced rework, fewer rejections, and labour hours redirected to higher-value work.

“mbue Submittals removes a long-standing bottleneck for subcontractors,” said Jean-Pierre Trou, founder and CEO of mbue. “By compressing weeks of paperwork into minutes and improving the accuracy of what gets submitted, we’re giving contractors a faster, more reliable path to approval so they can focus on building, not busywork.”

“Submittals have traditionally been a painful, labour-intensive process that slows projects down.” said Garrett White, VP, Texas at Weifield Group.

“With mbue, we’re seeing submittals completed in a fraction of the time, with greater accuracy and confidence. This is a huge step forward for the industry and directly helps us increase the amount of volume each PM can deliver, we can do more with the same team which directly contributes to our company’s revenue growth.”

mbue Submittals is initially focused on commercial electrical contractors, with access expanding to all subcontractors in future releases.

Pilots are underway with leading electrical contractors, including Weifield Group.

The post mbue launches AI-powered submittals appeared first on AEC Magazine.