It’s rare to see an architectural practice align itself so openly with a specific technology. But Studio Tim Fu is breaking that mould. Built from the ground up as an AI-first practice, the London-based studio is unapologetically committed to exploring how generative AI can reshape architecture—from the earliest concepts to fully constructable designs.

“We want to explore in depth how we can use the technology of generative AI, of neural networks, deep learning, and large language models as well, in an effort to facilitate an accelerated way of designing and building, but also thinking,” explains founder Tim Fu.

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Studio Tim Fu’s current methodology uses AI early in the design process to boost creativity, accelerate visualisation, and improve client communication — all while maintaining technical feasibility.

The technological journey began during Fu’s time at Zaha Hadid Architects, where he explored the potential of computational design to rationalise complex geometries. “We were thinking about the complexity of design and how we can bring that to fruition through computational processes and technologies,” he recalls.

This early exploration laid the groundwork to the Studio’s current AI-driven approach, which involves a sophisticated iterative process that blends human intention with machine learning capabilities. Initial AI-generated concepts are refined through human guidance, then reinterpreted by diffusion AI technology. This creates a dynamic feedback loop for rapid conceptualisation, where hundreds of design expressions can be explored in a single day.

Once we figure out the architectural design and planning that solves real life situation and constraints and context, we bring those back into the AI visualising models, to visualise and continue to iterate over our existing 3D models

Fu’s technical approach employs a complex system of AI tools, from common text-to-image generators such as Midjourney, Dall-E and Stable Diffusion to custom-trained models. Using these tools at the start of a project presents a ‘gradient of possibilities’, says Fu, both using AI’s creative agency and incorporating human intentions. The team uses text prompts to spark fresh ideas, producing ‘mood boards’ of synthetic visuals, as well as hand sketches to guide the AI.

“We use a mesh of back and forth with different design tools,” he explains. Ideas are generated and refined before they are translated into 3D geometry using modelling tools like Rhino.

“Once we figure out the architectural design and planning that solves real life situation and constraints and context, we bring those back into the AI visualising models, to visualise and continue to iterate over our existing 3D models,” he says. This enables the design team to see, for example, different possible expressions of window details and geometries. It’s a continuous loop—a creative dialogue between human intention and machine imagination.

Fu believes the results speak for themselves: in just one week, his team can deliver high-quality, client-ready concepts that far exceed what’s possible using conventional methods within the same time frame.

This level of efficiency brings new economic opportunities. Studio Tim Fu can charge clients less than traditional architects while boosting its earnings, all within conventional pricing structures. “We can lower the price because we can, and we can up the value, so it’s a win for the client and it’s good for us,” he says.

AI meets heritage

The Studio’s work on the Lake Bled Estate masterplan in Slovenia, its first fully AI-driven architectural project, serves as a landmark demonstration of these technical capabilities.

Spanning an expansive 22,000 square metre site, the project comprises six ultra-luxury villas set alongside the historic Vila Epos, a protected cultural monument of the highest national significance.

To produce a design that respects its historical context while creating an elevated luxury space, Studio Tim Fu synthesises heritage data with AI.

The Studio captured the local architectural vernacular by analysing material characteristics and extracting geometric parameters to comply with strict heritage regulations, including roof layout, height, and slope.

“This is the first time we are showing AI in its most contextually reflective way,” says Fu, “Something that is contrary to all the AI experiments that have come out since the dawn of diffusion AI processes.

“We want to showcase that this whole diffusion process can be completely controlled under our belt and be used for specifically addressing those issues [of respecting historical context].”

Delivering the details

Studio Tim Fu currently applies AI primarily at the concept-to-detail design stage. However, Fu believes we’re at a pivotal moment where AI is poised to take on more technical aspects of architectural design—particularly in areas like BIM modelling and dataset management.

“Because these are technical requirements, technical needs, and technical goals, it’s something that can be quantified,” he explains. “If it’s maximising certain functionality, while minimising the use of material and budget, these are numerical data that can be optimised. We’re just beginning that process of developing artificial general intelligence.”

But where does this leave humans? While Fu acknowledges that we must humbly recognise our limitations, he believes that human specialists—architects, designers, and fabricators—will remain essential, each working with AI within their own domain. At the same time, he sees enormous potential for AI to unify these fields.

“What AI can do is bring all of the human processes into a cohesive, streamlined decision making, to design to production process, because that’s what AI is good at. It’s good at cohesing large data sets, it’s good at addressing macro scale and micro scale values in the same time.”

Main image: Lake Bled Estate masterplan in Slovenia. Credit: Studio Tim Fu

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As a structural engineer, I’ve watched how AI is transforming various industries with excitement. But I’ve also noticed our field’s hesitation to adopt these technologies — and for good reason. We deal with safety-critical systems where reliability is a requirement.

In this article, I’ll show you how we can harness AI’s capabilities while maintaining the reliability we need as engineers. I’ll demonstrate this with an AI agent I created that can interpret truss drawings and run FEM analysis (code repository included), and I’ll give you resources to create your own agents.

The possibilities here have me truly excited about our profession’s future! I’ve been in this field for years, and I haven’t been this excited about a technology’s potential to transform how we work since I first discovered parametric modelling.

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What makes AI agents different?

Unlike traditional automation that follows fixed rules, AI agents can understand natural language, adapt to different situations, and even solve problems creatively. Think of them as smart assistants that can understand what you want and get it done.

For example, while a traditional Python script needs exact coordinates, boundary conditions, and forces to analyse a truss, an AI agent can look at a hand-drawn sketch or AutoCAD drawing and figure out the structure’s geometry by itself (see image below). It can even request any missing information needed for the analysis. This flexibility is powerful, but it also introduces unpredictability — something we engineers typically try to avoid.

The rise of specialised AI agents It’s 2025, and you’ve probably heard of ChatGPT, Claude, Llama, and other powerful Large Language Models (LLMs) that can do amazing things, like being incredibly useful coding assistants. However, running these large models in production is expensive, and their general-purpose nature sometimes makes them underperform in specific tasks.

This is where specialised agents come in. Instead of using one large model for everything, we can create smaller, fast, focused agents for specific tasks — like analysing drawings or checking building codes. These specialised agents are:

• More cost-effective to run
• Better at specific tasks
• Easier to validate

Agents are becoming the next big thing. As Microsoft CEO Satya Nadella points out, “We’re entering an agent era where business logic will increasingly be handled by specialised AI agents that can work across multiple systems and data sources”.

For engineering firms, this means we can create agents that understand our specific workflows and seamlessly integrate with our existing tools and databases.

The engineering challenge

Here’s our core challenge: while AI offers amazing flexibility, engineering demands absolute reliability. When you’re designing a bridge or a building, you need to be certain about your calculations. You can’t tell your client “the AI was 90% sure this would work.”

On the other hand, creating a rule-based engineering automation tool that can handle all kinds of inputs and edge cases while maintaining 100% reliability is a significant challenge. But there’s a solution.

Bridging the gap: reliable AI agents

We can combine the best of both worlds by creating a system with three key components (see image below):

1. AI agents handle the flexible parts – understanding requests, interpreting drawings, and searching for data.
2. Validated engineering tools perform the critical calculations.
3. Human in the loop: You, the engineer, maintain control — verifying data, checking results, and approving modifications.

Let me demonstrate this approach with a practical example I built: a truss analysis agent.

Engineering agent to analyse truss structures

Just as an example, I created a simple agent that calculates truss structures using the LLM Claude Sonnet. You give it an image of the truss, it extracts all the data it needs, runs the analysis, and gives you the results.

You can also ask the agent for any kind of information, like material and section properties, or to modify the truss geometry, loads, forces, etc. You can even give it some more challenging problems, like “Find the smallest IPE profile so the stresses are under 200 MPa”, and it does!

The first time I saw this working I couldn’t help but feel that childlike excitement engineers get when something cool actually works. Here is where you start seeing the power of AI agents in action.

It is capable of interpreting different types of drawings and creating a model, which saves a lot of time in comparison with the typical Python script where you would need to enter all the node coordinates by hand, define the elements and their properties, loads, etc.

Additionally, it solves problems using information I did not define in the code, like the section properties of IPE profiles or material properties of steel, or what is the process to choose the smallest beam to fulfil the stress requirement. It does everything by itself. N.B. You can find the source code of this agent in the resources section at the end.

In the video below, you can see the app I made using VIKTOR.AI

How does it work: an overview

Now let’s look behind the screen to understand how our AI agent works, so you can make one yourself.

In the image below you can see that in the centre you have the main AI agent, the brains of the operation. This is the agent that chats with the user and accepts text and images as input.

Additionally, it has a set of tools at its disposal, including another AI Agent, which it uses when it believes they are needed to complete the job:

• Analyse Image: AI Agent specialised in interpreting images of truss structures and returning the data needed to build the FEM model.
• Plot Truss: A simple Python function to display the truss structures.
• FEM Analysis: Validated FEM analysis script programmed in Python.

The Main agent

The Main agent is powered by Claude 3.7 Sonnet, which is the latest LLM provided by Anthropic. Basically, you are using the same model you are chatting with when using Claude in the browser, but you use it in your code using their API, and you give the model clear guidelines on how to behave and provide it with a set of tools it can use to solve problems.

You can also use other models like ChatGPT, Llama 3.x, and more, as long as they support tool calling natively (using functions). Otherwise, it gets complicated to use your validated engineering scripts.

For example, here’s how we get an answer from Claude using Python (see image below).

Let’s break down these key components:

• SYSTEM MESSAGE: This is a text that defines the agent’s role, behaviour guidelines, boundaries, etc.
• TOOLS_DESCRIPTION: Description of what tools the agent can use, their input and output.
  messages: This is the complete conversation, including all previous user and assistant (Claude) messages, so Claude knows the context of the conversation.

Tools use

One of the most powerful features of Claude and other modern LLMs is their ability to use tools autonomously. When the agent needs to solve a problem, it can decide which tools to use and when to use them. All it needs is a description of the available tools, like in the image below.

The agent can’t directly access your computer or tools — it can only request to use them. You need a small intermediary function that listens to these requests, runs the appropriate tool, and sends the results back. So don’t worry, Claude won’t take over your laptop… yet

The Analyse image agent

Here’s a fun fact: the agent that analyses truss images is actually another instance of Claude! So yes, we have Claude talking to Claude (shhh…. don’t tell him ). I did this to show how agents can work together, and honestly, it was the simplest way to get the job done.

This second agent uses Claude’s ability to understand both images and text. I give it an image and ask it to return the truss data in a specific JSON format that we can use for FEM analysis. Here is the prompt I use.

I’m actually quite impressed by how well Claude can interpret truss drawings right out of the box. For complex trusses, though, it sometimes gets confused, as you can see in the test cases later.

This is where a specialised agent, trained specifically for analysing truss images, would make a difference. You could create this using machine learning or by fine-tuning an LLM. Fine-tuning means giving the model additional training on your specific type of data, making it better at that task (though potentially worse at others).

Test case: book example

The first test case is an image of a book (see image below). What’s interesting is that the measurements and forces are given with symbols, and then the values are provided below. You can also see the x and y axis with arrows and numbers, which could be distracting.

The agent did a very good job. Dimensions, forces, boundary conditions, and section properties are correct. The only issue is that element 8 is pointing in the wrong direction, which is something I ask the agent to correct, and it did.

Test case: AutoCAD drawing

This technical drawing has many more elements than the first case (see image below). You can also see many numerical annotations, which could be distracting.

Again, the agent did a great job. Dimensions and forces are perfect. Notice how the agent understands that, for example, the force 60k is 60,000 N. The only error I could spot is that, while the supports are placed at the correct location, two of them should be rolling instead of fixed, but given how small the symbols are, this is very impressive. Note that the agent gets a low-resolution (1,600 x 400 pixel) PNG image, not a real CAD file.

Test case: transmission tower

This is definitely the most challenging of the three trusses, and all data is in the text. It also requires the agent to do a lot of math. For example, the forces are at an angle, so it needs to calculate the x and y components of each force. It also needs to calculate x and y positions of nodes by adding different measurements like this: x = a + a + b + a + a.

As you can see in the image below, this was a bit too much of a challenge for our improvised truss vision agent, and for more serious jobs, we need specialist agents. Now, in defence of the agent, the image size was quite small (700 x 600 pixels), so maybe with larger images and better prompts, it would do a better job.

An open-source agent for you

I’ve created a simplified version of this agent that demonstrates the core concepts we’ve discussed. This implementation focuses on the essential components:

• A basic terminal interface for interaction
• Core functionality for truss analysis
• Integration with the image analysis and FEM tools

The code is intentionally kept minimal to make it easier to understand and experiment with. You can find it in this GitHub repository. This simplified version is particularly useful for:

• Understanding how AI agents can integrate with engineering tools
• Learning how to structure agent-based systems
• Experimenting with different approaches to truss analysis

While it doesn’t include all the features of the full implementation, it provides a solid foundation for learning and extending the concept. You can use it as a starting point to build your own specialised engineering agents. See video below.

Conclusions

After building and testing this truss analysis agent, here are my key takeaways:

1) AI agents are game changers for engineering workflows

• They can handle ambiguous inputs like hand-drawn sketches
• They adapt to different ways of describing problems
• They can combine information from multiple sources to solve complex tasks

2) Reliability comes from smart architecture

• Let AI handle the flexible, creative parts
• Use validated engineering tools for critical calculations
• Keep engineers in control of key decisions

3) The future is specialised

• Instead of one large AI trying to do everything
• Create focused agents for specific engineering tasks
• Connect them into powerful workflows

4) Getting started is easier than you think

• Modern LLMs provide a great foundation
• Tools and APIs are readily available
• Start small and iterate

Remember: AI agents aren’t meant to replace engineering judgment — they’re tools to help us work more efficiently while maintaining the reliability our profession demands. By combining AI’s flexibility with validated engineering tools and human oversight, we can create powerful solutions that respect our professional responsibilities.

I hope you’ll join me in exploring what’s possible!

Resources

• Simple Truss Analysis agent Project repository: GitHub
• Claude: Learn promping, tool calling and multimodality (free course)
• LLama 3.2: Learn promping, tool calling and multimodality (free course)
• Claude documentation on tool use
• Claude documentation on vision

About the author

Anande Bergman is a product strategist and startup founder who has contributed to multiple successful tech ventures, including a globally-scaled engineering automation platform.

With a background in aerospace engineering and a passion for innovation, he specialises in developing software and hardware products and bringing them to market.

Drawing on his experience in both structural engineering and technology, he writes about how emerging technologies can enhance professional practices while maintaining industry standards of reliability.

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It’s tough being one of the first to enter a market but for Space, one of the country’s most BIM-centric architectural practices, it was a case of needs must. In 2016, its BIM consultancy spin-off, BIM Technologies, identified a need for its clients to be able to access their model data without the need for expensive software or hardware. Development started and this eventually became Twinview, launched in 2019.

Space Group is a practicing architecture firm, a BIM software developer, a services supplier, a BIM components / objects library creator and distributor. So, not only does it develop BIM software, it also uses the software in its own practice, as well as sell its solutions and services to other firms.

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Selling twins

In previous conversations with CEO Rob Charlton on the market’s appetite for digital twins, he has been frank in the difficulty in getting buy in from fellow architects, developers and even owner operators. The customers who got into twins early were firms that owned portfolios of buildings which were sold as eco-grade investments.

Charlton acknowledges that he always expected it to be a long-term endeavour, “We started this development knowing it was it was a five year plus journey to any level of maturity or even awareness”. He draws a parallel to the adoption of BIM, recalling that even though Space bought its first license of Revit around 2001, it didn’t gain significant traction until around 2011, and even then, this was largely due to UK BIM mandates.

The early digital twin market development was a ‘slow burn’. Charlton contrasts BIM Technologies’ patient, self-funded approach with companies that seek large VC funding, arguing that “ the market will move at the level it’s ready for”.

He explains that the good news is that over the last year, there has been an increase in awareness of the value
of digital twins, particularly in the last six months.

This awareness is seen in the fact that clients are now putting out Requests for Proposals (RFPs) for digital twin solutions. For Charlton, this is a fundamental difference compared to the past, where they would have to approach firms to explain the benefits of digital twins. Now, the clients themselves have made the decision that they want a digital twin and are seeking proposals from providers.

Priorities and needs

There’s a lot of talk about digital twins but very little talk concerning the actual benefits of investing in building them. Charlton explains a lot of twin clients are increasingly interested in reducing carbon in buildings, whether that be in embodied or operational and compliance and safety. “It’s an area that Space is particularly passionate about but there is an inconsistency in how embodied carbon reviews and measurements are conducted,” he says.
Customer access to operational data is also important, explains Charlton, “Clients want to gain insights into how their buildings are actually performing in real time.”

He also notes that the facilities and the integration with facilities management is equally important, to streamline maintenance, manage issues, and improve overall building operations.

Clients value the ability to have “access to their information in one place” adds Charlton. And here, the cloud is the perfect solution to deliver a unified platform which consolidates models, and documents related to building assets.

Twinview clients are especially interested in owning their own data. Charlton gives the example of a New Zealand archive project, explaining that the client was particularly interested in having Twinview to maintain independence when using a subcontractor or external service provider, which might come and go over the project lifetime.

Back in the UK, Twinview is being used in conjunction with ‘desk sensors’ on an NHS project to optimise space and potentially avoid unnecessary capital expenditure. Charlton explains that the client was finding the digital twin useful for “analysis on how the space is used” because they were seeking to validate or challenge space needs assessments by consultants.

Increasingly, contractual obligations include performance data. For one of Space’s school clients, the DFA Woodman Academy, there’s a contractual obligation to provide energy performance data at month, three months and 12-months. Digital twin technology facilitated the compliance goal within the performance-based contract. The IoT sensors also identified high levels of CO2 in the classrooms, prompting an investigation into the cause.
Twinview goes beyond the traditional digital twin model for operations and has been used to connect residents to live building information. On a residential project, tenants access the Twinview data on their mobile phones to see energy levels in the buildings, temperatures and CO2, all through their own app.

Artificial Intelligence

Everyone is talking about AI, and Twinview now features a ChatGPT-like front end. This enables plain language search within the digital twin, both at an asset level and with regards to performance data. Charlton explains that while the AI in Twinview has a ‘ChatGPT-like interface’, it is not directly ChatGPT, although it does connect to it. He explains that Twinview developed its own system. This is possibly due to the commercial costs associated with using ChatGPT for continuous queries. The AI in Twinview accesses all building information, including the model, operational data, and tickets, which are stored in a single bucket on AWS. Looking to the future, Charlton mentions that the next stage of AI development for Twinview will be focused on prediction and learning. This includes the ability to generate reports automatically (e.g. weekly on average CO2 levels), predict future energy usage, and suggest ways to improve building performance. A key differentiator for AI in Twinview in the future, will be in its capacity to understand correlations between disparate datasets that are often siloed, such as occupancy data, fire analysis, and energy consumption. By applying a GPT-like technology over this connected data, the aim is to uncover new insights and solutions.

Development Journey

From a slow burn start, despite being a relatively small UK business and competing with big software firms with
deep pockets, Charlton told us that Twinview had already won international clients and is currently being
shortlisted for other significant international projects, including one on the west coast of America, against international competition.

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The race to challenge Autodesk Revit with next-generation BIM tools has intensified with the launch of Motif, a startup that has just emerged out of stealth. Motif joins other startups including Arcol, Qonic, and Snaptrude, who are already on steady development paths to tackle collaborative BIM. However, like any newcomer competing with a well-established incumbent, it will take years to achieve full feature parity. This is even the case for Autodesk’s next generation cloud-based AEC technology, Forma.

What all these new tools can do quickly, is bring new ideas and capabilities into existing Revit (RVT) AEC workflows. This year, we’re beginning to see this happening across the developer community, a topic that will be discussed in great detail at our NXT BLD and NXT DEV conferences on 11 and 12 June 2025 at the Queen Elizabeth II Centre in London.

Though a late entrant to the market, Motif stands out. It’s led by Amar Hanspal and Brian Mathews, two former Autodesk executives who played pivotal roles in shaping Autodesk’s product development portfolio.

Hanspal was Autodesk CPO and, for a while, joint CEO. Mathews was Autodesk VP platform engineering / Autodesk Labs and lead the industry’s charge into adopting reality capture. They know where the bodies are buried and have decades of experience in software ideation, running large teams and have immediate global networks with leading design IT directors. Their proven track record also makes it easier for them to raise capital and be taken as a serious contender from the get-go.

Further reading – Motif V1: our first thoughts

Motif

In late January, the company had its official launch alongside key VC investors. Motif secured $46 million in seed and Series A funding. The Series A round was led by CapitalG, Alphabet’s independent growth fund, while the seed round was led by Redpoint Ventures. Pre-seed venture firm Baukunst also participated in both rounds. This makes Motif the second largest funded start-up in the ‘BIM’ space – the biggest being HighArc, a cloud-based expert system for US homebuilders, at $80 million.

While Motif has been in stealth for almost two years, operating under the name AmBr (we are guessing for Amar and Brian). Major global architecture firms have been involved in shaping the development of the software, even before any code was written, all under strict NDAs (Non-disclosure Agreements).

The firms working with Hanspal’s team deliver the most geometrically complex and large projects. The core idea is that by tackling the needs of signature architectural practices, the software should deliver more than enough capability for those who focus on more traditional, low risk designs.

There is considerable appetite to replace the existing industry standard software tools. This hunger has been expressed in multiple ‘Open Letters to Autodesk’, based on a wish for more capable BIM tools – a zeitgeist which Motif is looking to harness, as BIM eventually becomes a replacement market.

The challenge

Motif’s mission is to modernise the AEC software industry, which it sees as being dominated by ‘outdated 20th-century technology’. Motif aims to create a next-generation platform for building design, integrating 3D, cloud, and machine learning technologies. Challenges such as climate resilience, rapid urbanisation modelling, and working with globally distributed teams will be addressed, and the company’s solutions will integrate smart building technology.

Motif will fuse 3D, cloud, and AI with support for open data standards within a real-time collaborative platform, featuring deep automation. The unified database will be granular, enabling sharing at the element level. This, in many ways follows the developments of other BIM start-ups such as Snaptrude and Arcol, which pitch themselves as the ‘Figma’ for BIM. In fact, Hanspal was an early investor in Arcol, alongside Procore’s Tooey Courtemanche.

At the moment, there is no software for the public to see, just some hints of the possible interface on the company’s website. Access is request only. AEC Magazine is not privy to any product demonstrations, only what we have gleamed through conversations with Motif employees. The launch provided us with an exclusive interview with Hanspal to discuss the company, the technology and what the BIM industry needs.

A quantum of history

Before we dive into the interview, let’s have a quick look at how we got here. At Autodesk University 2016, while serving as Autodesk’s joint CEO, Hanspal introduced his bold vision for the future of BIM. Called Project Quantum, the aim was to create a new platform that would move BIM workflows to the cloud, providing a common data environment (CDE) for collaborative working.

Hanspal aimed to address problems which were endemic in the industry, arising from the federated nature of Architecture, Engineering, and Construction (AEC) processes and how software, up to that point, doubled down on this problem by storing data in unconnected silos.

Instead of focusing on rewriting or regenerating Revit as a desktop application, the vision was to create a cloud-based environment to enable different professionals to work on the same project data, but with different views and tools, all connected through the Quantum platform.

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Quantum would feature connecting workspaces, breaking down the monolithic structure of typical AEC solutions. This would allow data and logic to be accessible anywhere on the network and available on demand, in the appropriate application for a given task. These workspaces were to be based on professional definitions, providing architects, structural engineers, MEP (Mechanical, Electrical, and Plumbing) professionals, fabricators, and contractors with access to the specific tools they need.

Hanspal recognised that interoperability was a big problem, and any new solution needed to facilitate interoperability between different software systems, acting as a broker, moving data between different data silos. One of the key aspects of Quantum was that the data would be granular, so instead of sharing entire models, Quantum could transport just the components required. This would mean users receive only the information pertinent to their task, without the “noise” of unnecessary data.

Eight months later, the Autodesk board elected fellow joint CEO, Andrew Anagnost as Autodesk CEO and Hanspal left Autodesk. Meanwhile, the concept of Quantum lived on and development teams continued exploratory work under Jim Awe, Autodesk’s chief software architect.

Months turned into years and by 2019, Project Quantum had been rebranded Project Plasma, as the underlying technology was seen as a much broader company-wide effort to build a cloud-based data-centric approach to design data . Ultimately, Autodesk acquired Spacemaker in 2020 and assigned its team to develop the technology into Autodesk Forma, which launched in 2023—more than six years after Hanspal first introduced the Quantum concept.

However, Forma is still at the conceptual stage, with Revit continuing to be the desktop BIM workflow, with all its underlying issues.

In many respects, Hanspal predicted the future for next generation BIM in his 2016 Autodesk University address. Up until that point Autodesk had wrestled for years with cloud-based design tools, with its first test being Mechanical CAD (MCAD) software, Autodesk Fusion, which demoed in 2009 and shipped in 2013. Cloud-based design applications were a tad ahead of the web standards and infrastructure which have helped product like Figma make an impact.

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In conversation

On leaving Autodesk in 2017, after his 15+ year stint, Hanspal thought long and hard about what to do next. In various conversations over the years, he admitted that the most obvious software demand was for a new modern-coded BIM tool, as he had proposed in some detail with Quantum. However, Hanspal was mindful that it might be seen as sour grapes. Plus, developing a true Revit competitor came with a steep price tag—he estimated it would take over $200 million. Instead, Hanspal opted to start Bright Machines, a company which delivers the scalable automation of robot modules with control software which uses computer vision machine learning to manufacture small goods, like electronics.

After almost four years at Bright Machines, in 2021, Hanspal exited and returned to the AEC problem, which, in the meantime, had not made any progress. During COVID, AEC Magazine was talking with some very early start-ups, and pretty much all had been in contact with Hanspal for advice and/or stewardship.

Martyn Day: Your approach to the market isn’t a single-platform approach, like Revit?

Amar Hanspal: In contrast to the monolithic approach of applications like Revit, we aim to target specific issues and workflows. There will be common elements. With the cloud, you build a common back end, but the idea is that you solve specific problems along the way. You only need one user management system, one payment system, collaboration etc. There are some technology layers that are common. But the idea is about solving end-user problems like design review, modelling, editing, QA, QC.

This isn’t a secret! I talked about this in the Quantum thing seven years ago! I always say ideas are not unique. Execution is. When it comes down to it, can anybody else do this? Of course they can. Will they do this? Of course not!

Martyn Day: Data storage and flow is a core differential from BIM 2.0. Will your system use granular data, and how will you bypass limitations of browser-based applications. You talk about ‘open’, which is very in vogue. Does that mean that your core database is Industry Foundation Classes (IFC), or is there a proprietary database?

Amar Hanspal: There are three things we have to figure out. One how to run in a browser, where you have the limited memory, so you can’t just send everything. You’ve got to get really clever about how to figure out what [data] people receive – and there’s all sorts of modern ways of doing that.

Second is you have to be open from the get-go. However we store the data, anybody should be able to access it, from day one.

And then the third thing is, you can’t assume that you have all the data, so you have to be able to link to other sources and integrate where it makes sense. If it’s a Revit object, you should be able to handle it but if it’s not, you should be able to link to it.

You have to do some things for performance – it’s not proprietary, but you’re always doing something to speed up your user experience. The one path is, here’s your client, then you have to get data fast to them, and you have to do that in a very clever way, all while you’re encrypting and decrypting it. That’s just for user experience and performance, but from a customer perspective, anytime you want to interrogate the data send and request all the objects in the database – there is a very standard web API that you can use, and it’s always available.

Of course we’ll support IFC, just like we support RVT and all these formats. But that’s not connected, not our core data format. Our core data format is a lot looser, because we realised in this industry, it’s not just geometric objects you’re dealing with, you must deal with materials, and all sorts of data types. In some ways, you must try and make it more like the internet in a way. Brian [Mathews] would explain that the internet is this kind of weirdly structured yet linked data, all at the same time. And I think that’s what we are figuring out how to do well.

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Martyn Day: We have seen all sorts of applications now being developed for the web. Some are thick clients with a 20 GB download – basically a desktop application running in a web browser, utilising all the local compute, with the data on the cloud. Some are completely on the cloud with little resource requirement on the local machine. Autodesk did a lot of experimentation to try and work out the best balance. What are you doing?

Amar Hanspal:  It’s a bit of a moving edge right now. I would say that you want to begin first principles. You want to get the client as thin as possible so that if you can, you avoid the big download at all costs. That can be through trickery, it’s also where WebGPU and all these new things that are showing up are helping. You can start using browsers for more and more [things] every day that will help deliver applications. But I do think that there are situations in which the browser is going to get overwhelmed, in which case, you’re going to require people to add something. Like, when the objects get really large and very graphical, sometimes you can deliver a better user experience if you give somebody a thicker client.  I think that’s some way off for us to try and deal with, but our first principle is to just leverage the browser as much as possible and not require users to download something to use our application. I think it may become, ‘you hit this wall for this particular capability’, then you’ll need to add something local.

Martyn Day: You have folks that have worked on Revit in your team. Will this help your RVT ability form the get go?

Amar Hanspal: We’ve not reverse engineered the file format, but, you know, we do know how this works. We’re staying good citizens and will play nice. We’re not doing any hacks, we’re going to integrate very cleanly with whatever – Revit, Rhino, other things that people use – in a very clean way. We’re doing it in an intelligent way, to understand how these things are constructed.

Martyn Day: The big issue is that Revit is designed to predominantly model, in order to produce drawings. Many firms are fed up with documentation and modelling to produce low level of detail output. Are you looking to go beyond the BIM 1.0 paradigm?

Amar Hanspal: Yes, fabrication is very critical for modular construction. Fabrication is really one of the things that you have to ‘rethink’ in some way. It’s probably the most obvious other thing that you have to do. I also think that there are other experiences coming out, not that we are an AR/VR play, but you’re creating other sorts of experiences, and deliverables that people want like. We need to think through that more expansively.

Martyn Day: Are you using a solid modelling engine underneath, like Qonic?

Amar Hanspal: Yes, there is an answer to that, but what we’re coming out with first, won’t need all that complexity, but yeah, of course, we will do all that stuff over time.  There is a mixture of tech that we can use – off the shelf – like license one or use something that is relatively open source.

Martyn Day: Most firms who have entered this space, taking on Revit, is the software equivalent of scaling the North face of the Eiger – 20 years of development, multidiscipline, broadly adopted. All of the new tools initially look like SketchUp, as there’s so much to develop. Some have focused on one area, like conceptual, others have opted to develop all over the place to have broad, but shallow functionality. Are you coming to market focussing on a sweet spot?

Amar Hanspal:  One of the things we learned from speaking to customers is that [in] this whole concept modelling / Skema / TestFit world there are so many things that developers are doing. We’re going after a different problem set. In some ways, the first thing that we’re doing will feel much more like a companion, collaboration product, and it will look like a creation thing. I don’t want to take anything out of market that feels half incomplete. The lessons we’ve learned from everything is that even to do the MVP (Minimum Viable Product) in modelling, we will be just one of sixteen things that people are using. I think, you know, I’d much rather go up to the North face and scale it.

Martyn Day: Many of the original letter writers were signature architects, complaining that they couldn’t model the geometry in Revit so used Rhino / Grasshopper then dropped the geometry into Revit. So, are you talking to the most demanding group of users to please?

Amar Hanspal:  I 100% agree with you. I think someone has to go up the North face of the Eiger. That’s my thing, it’s the hardest thing to do. It’s why we need this special team. It’s why we need this big capital. That’s why Brian and I decided to do it. I was thinking, who else is going to do it? Autodesk isn’t doing it! This Forma stuff isn’t really leading to the reinvention of Revit.

All these small developers that are showing up, are going to the East face. I give them credit. I’m not dissing them, but if they’re not going to scale the North face… I’m like, OK, this is hard, but we have got to go up the North face of the Eiger, and that’s what we’re going to do.

It’s like Onshape [cloud-based MCAD software] took ten years. Autodesk Fusion took ten years. And this might take us ten years to do it – I don’t think it will. So, what you will see from us – and maybe you might even criticise us for – is while we’re scaling, it’s going to look like little, tiny subsets coming out. But there’s no escaping the route we have to go.

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Martyn Day: From talking with other developers, it looks like it will take five years to be feature comparative. The problem is products come to the market and aren’t fleshed out, they get evaluated and dismissed because they look like SketchUp, not a Revit replacement and it’s hard to get the market’s attention again after that.

Amar Hanspal:  Yeah, I think it’s five years. And that’s why, deliberately, the first product that’s going to come out is not going to be the editor. It’s going to look a little bit more Revizto-like because I think that’s what gives us time to go do the big thing. If you’re gonna come for the King, you better not miss. We’ve got to get to that threshold where somebody looks at it and goes, ‘It doesn’t do 100% but it does 50% or 60%’ or I can do these projects on it and that’s where we are – it’s why we’re working [with] these big guys to keep us honest. When they tell us they can really use this, then we open it up to everybody else. Up until then, we’ll do this other thing that is not a concept modeller but will feel useful.

Martyn Day: How many people are in the team now?

Amar Hanspal:  We’re getting 35 plus. I think we’re getting close to 40. It’s mostly engineering people. Up until two weeks ago, it was 32 engineers and myself. Now I have one sales guy, one marketing, so we’ll have a little bit of go to market. But it’s mainly all product people. We are a distributed company, based around Boston, New York or the Bay Area – that’s our core.

We’re constructing the team with three basic capabilities. There’s classic geometry, folks – and these are the usual suspects. The place where we have newer talent is on the cloud side, both on trying to do 3D on the browser front end, and then on the back-end side, when we’re talking about the data structures. None of those people come from CAD companies, none of them, they are all Twitter, Uber or robotics companies – different universes to traditional CAD.

The third skill set that we’re developing is machine learning. Again, none of those guys are coming from Cloud or 3D companies. These are research-focused, coming from first principles, that kind of focus.

Martyn Day: By trying to rethink BIM and being heavily influenced by what came before, like Revit, is there a danger of being constrained by past concepts? Somone described Revit to me as 70s thinking in 80s programming. Obviously now computer science, processors, the cloud have all moved on. The same goes for business models. This weekend, I watched the CEO of Microsoft say SaaS was dead!

Amar Hanspal:  We know we’re living in a post subscription world. Post ‘named user’ world is the way I would describe it. The problem with subscription right now, is that it’s all named user, you’ve got to be onboard, and then this token model at Autodesk is if you use the product for 30 seconds, then you get charged for the whole day.

It’s still very tied to, sort of like a human being in front in a chair. That’s what makes the change. Now, what does that end up looking like? You know the prevalent model, there’s three that are getting a lot of interest: one is the Open AI ChatGPT model. It’s get a subscription, you get a bunch of tokens. You exceed them, you get more.

The other one, which I don’t think works in AEC, is outcome-based pricing, which works for callcentres. You close a call, you create seven bucks for the software. I don’t see that happening. What’s the equivalent in AEC time? Produce drawing, seven bucks? What is the equivalent of that? That just seems wrong. I think we’re going to end up in this somewhat hybrid tokenised / ChatGPT style model, but you know we have to figure that out. We have to account for people’s ability to flex up and down. They have work what comes in and out. Yeah, that’s the weakness of the subscription business model, is that customers are just stuck.

Martyn Day: Why didn’t Autodesk redevelop Revit in the 2010 to 2015?

Amar Hanspal:  What I remember of those days – it’s been a while – is I think there was a lot of focus on just trying to finish off Revit Structure and MEP. I think that was the one Revit idea, and then suites and subscriptions. There was so much focus on business models on that. But you’re right. I think looking back, that was the time we should have have redone Revit. I started to it with Quantum, but I didn’t last long enough to be able to do it!

Conclusion

One could argue that the decision by Autodesk not to rewrite Revit and minimise the development was a great move, profit-wise. For the last eight years, Revit sales haven’t slowed down and copies are still flying off the shelves. Revit is a mature product with millions of trained users and RVT is the lingua franca of the AEC world, as defined in many contracts. There is proof to the argument that software is sticky and there’s plenty of time with that sticky grip, for Autodesk to flesh out and build its Forma cloud strategy.

Autodesk has taken active interest in the start ups that have appeared, even letting Snaptrude exhibit at Autodesk University, while it assesses the threat and considers investing in or buying useful teams and tech. If there is one thing Autodesk has, it’s deep pockets and throughout its history has bought each subsequent replacement BIM technology – from Architectural Desktop (ADT) to Revit. Forma would have been the first in-house development, although I guess that’s partially come out of the SpaceMaker acquisition.

But this isn’t the whole story. With Revit, it’s not just that the software that is old, or the files are big, or that the Autodesk team has given up on delivering major new productivity benefits. From talking with firms there’s an almost allergic reaction to the business model, coupled with the threat of compliance audits, added to the perceived lack of product development. In the 35+ years of doing this, it’s still odd seeing Autodesk customers inviting in BIM start-ups to try and help the competitive products become match-fit in order to provide real productivity benefits – and this has been happening for two years.

With Hanspal now throwing his hat officially in the ring, it feels like something has changed, without anything changing. The BIM 2.0 movement now has more gravitas, adding momentum to the idea that cloud-based collaborative workflows are now inevitable.  This is not to take anything away from Arcol, Snaptrude and Qonic which are possibly years ahead of Motif, having already delivered products to market, with much more to come.

From our conversation with Hanspal, we have an indication of what Motif will be developing without any real physical proof of concept. We know it has substantial backing from major VCs and this all adds to the general assessment that Revit and BIM is ripe for the taking.

At this moment in the AEC space, trying to do a full-frontal assault of the Revit installed-base, is like climbing North Face of the Eiger – you better take a mighty big run up and have plenty of reserves. And, for a long time, it’s going to look like you are going nowhere. Here, Motif is playing its cards close to its chest, unlike the other start-ups which have been sharing in open development from very early on, dropping new capabilities weekly. While it is clear to assess the velocity with which Snaptrude, Arcol and Qonic deliver, I think it’s going to be hard to measure Motif’s modeller technology until it’s considerably along in the development phase. It’s a different approach. It doesn’t mean it’s wrong and with regular workshops and collaboration with the signature architects, there should be some comfort for investors that progress is being made. But, as Hanspal explained, it’s going to be a slow drip of capability.

While Autodesk may have been inquisitive about the new BIM start-ups, I suspect the ex-Autodesk talent in Motif, carrying out a similar Quantum plan, would be seen as a competitor that might do some damage if given space, time and resources. Motif is certainly well funded but with a US-based dev team, it will have a high cash burn rate.

By the same measurement, Snaptrude is way ahead, has a larger, purely Indian development team, with substantially lower costs and lower capital burn rate. Arcol has backing from Tooey Courtemanche (aka Mr. Procore) and Qonic is doing fast things with big datasets that just look like magic and have been totally self-funded. BIM 2.0 already has quality and depth. The challenge is to offer enough benefit, at the right price, to make customers want to switch, for which there is a minimal viable product.

It’s only February and we already know that this will be the year that BIM 2.0 gets real. All the key players and interested parties will all be at our NXT BLD and NXT DEV conferences in London on 11-12 June 2025 – that’s Arcol, Autodesk, Bentley Systems, Dassault Systèmes, Graphisoft, Snaptrude, Qonic and others. As these products are being developed, we need as many AEC firms onboard to helping guide their direction. We need to ensure the next generation of tools are what is needed, not what software programmers think we need, or limited to concepts which constrained workflows in the past. Welcome Motif to the melee for the hearts and minds of next generation users!

The post Motif to take on Revit: exclusive interview appeared first on AEC Magazine.

Planning is already underway for AEC Magazine’s annual, two day, dual-focus conference, NXT BLD (Next Build) and NXT DEV (Next Development), in conjunction with Lenovo workstations. The event will be held on 11 and 12 June 2025 at the prestigious Queen Elizabeth II Conference Centre in London.

Year on year, the NXT experience has grown in reputation, and we now attract design IT directors from multiple continents, together with a plethora of innovative start-ups looking to push the industry forward to next generation workflows and BIM 2.0.

NXT BLD brings innovative industry ideas, in-house development, new workflows and bleeding-edge technology to two conference stages, plus an exciting exhibition. Presentations range from design IT directors sharing insights into their processes to the latest in workstation, AR and VR technology.

Find this article plus many more in the Jan / Feb 2025 Edition of AEC Magazine

Subscribe FREE here

NXT DEV addresses the fact that the AEC technologies we use are at a crossroads. The industry is reliant on old software that doesn’t utilise modern processor architectures, while the benefits of combining cloud, database and granular data await with the next generation of tools. AEC professionals can’t leave it to software developers and computer scientists to deliver change and need to help shape what comes next. NXT DEV is a forum for discussion, a great way to meet the start-ups, venture capitalists (VCs) and fellow design IT directors who are eager to find more productivity and smarter tools.

AEC Magazine is inviting you to come, get inspired and join the discussion.

For more info visit www.nxtbld.com and www.nxtdev.build.
Early bird tickets with 20% discount are available until 15 April 2025.

Topics

We are early in the planning stages for the events but you can be sure that we will be talking about BIM 2.0, Autodrawings, AI, Generative Design, AR and VR, GIS and BIM, Open Source, Rapid Reality Capture, Expert Automation Systems, Digital Fabrication, the future of data and API access.

Talks

There will be inspirational presentations from Heatherwicks, Alain Waha (Buro Happold), Patrick Cozzi (Cesium, now Bentley Systems), Lenovo, Perkins and Will, Augmenta, Finch3D, Ismail Seleit (LoRA and ControlNet AI rendering), Antonio Gonzalez Viegas (ThatOpenCompany), Qonic, Snaptrude, Arcol, Gräbert (Autodrawings), Autodesk, Foster + Partners, and Jonathan Asher (Dassault Systèmes) – to name but a few.

More speakers will be announced in the coming weeks, as we shape the two-day NXT 2025 program. The editorial team are looking forward to seeing you there!

The two days of NXT offer an intense dive into the future of the industry. Simultaneous stages offer a breadth of topics and areas of interest, plus there’s plenty of exciting new technologies to see on the show floor. You would certainly benefit from bringing a team to ensure you don’t miss anything important.

NXT BLD 2025
Wednesday 11 June 2025

NXT DEV 2025
Thursday 12 June 2025

Queen Elizabeth II Centre
Westminster, London, UK

NXTAEC – inspirational presentations on demand

Presentations from previous NXT events are available to view free on our dedicated website – NXTAEC.com
Here are some highlights

The future AEC software specification
Aaron Perry
AHMM

Transforming the future of home construction
Bruce Bell & Oliver Thomas
Facit Homes

10 things you should know about developing AEC software products
Amar Hanspal
Motif

Synthesising design and execution
John Cerone
SHoP Architects

The post The NXT 2025 experience appeared first on AEC Magazine.

Founded in 2018 by Anthony Hauck and Ian Keough, Hypar has certainly been on a journey in terms of its public-facing aims and capabilities.

Both co-founders are well-established figures in the software field. Hauck previously led Revit’s product development and pioneered Autodesk’s generative design initiatives. Keough, meanwhile, is widely recognised as the creator of Dynamo, a visual programming platform for Revit.

Initially, their creation Hypar looked very much like a single, large sandpit for generative designers familiar with scripting, enabling them to create system-level design applications, as well as for nonprogrammers looking to rapidly generate layouts, duct routing and design variations and get feedback on key metrics, which could then be exported to Revit.

Find this article plus many more in the Jan / Feb 2025 Edition of AEC Magazine

Subscribe FREE here

Back in 2023, we were blown away with Hypar’s integration of ChatGPT at the front end. This aimed to give users the ability to rapidly generate conceptual buildings and then progress on to fabrication-level models. This capability was subsequently demonstrated in tandem with DPR Construction.

One year later and the company’s front end has changed yet again. With a whole new interface and a range of capabilities specifically focused on space planning and layout, it feels as if Hypar has made a big pivot. What was once the realm of scripters now looks very much like a cloud planning tool that could be used by anyone.

AEC Magazine’s Martyn Day caught up with the always insightful Ian Keough to discuss Hypar’s development and better understand what seems like a change in direction at the company, as well as to get his more general views on AEC development trends.

Martyn Day: Developers such as Arcol, Snaptrude and Qonic are all aiming firmly at Revit, albeit coming at the market from different directions and picking their own entry points in the workflow to add value, while supporting RVT. Since Revit is so broad, it seems clear that it will take years before any of these newer products are feature-comparable with Revit, and all these companies have different takes on how to get there. With that in mind, how do you define a nextgeneration design tool and what is Hypar’s strategy in this regard?

Ian Keough: At Hypar, we’ve been thinking about this problem for five or six years from a fundamentally different place. Our very first pitch deck for Hypar showed images from work done in the 1960s at MIT, when they were starting to imagine what computers would be used for in design. They weren’t imagining that computers would be used for drafting, of course. Ivan Sutherland had already done that years before and we have all seen those images.

I think there are a lot of people who have very uninteresting ideas around AI in architecture, and those involve things like using AI to generate renderings and stuff like that. It’s nifty to look at, but it’s so low value in terms of the larger story of what all this computing power could do for us – Ian Keough

What they were imagining is that computers would be used to design buildings, and they were making punch card programmes to lay out hospitals and stuff and that. To me, that’s a very pro-future kind of vision. It imagined that computing capacity would grow to a point where the computer would become a partner in the process of design, as opposed to a slightly better version of the drafting board.

However, when it eventually happened, AutoCAD was released in the 1980s and instead we took the other fork of history. The result of taking that other fork has been interesting. If you look at this from a historic perspective, computers did what they did and they got massively more powerful over the years. But the small layer on top of that was all of our CAD software, which used very little of that available computing power. In a real sense, it used the local CPU, but not the computing power of all the data centres around the world which have come online. We were not leveraging that compute power to help us design more efficiently, more quickly, more correctly. We were just complaining that we couldn’t visualise giant models, and that’s still a thing that people talk about.

That’s still a big problem for people’s workloads. I don’t want to dismiss it. If you’re building an airport, you have got to load it, federate all of these models and be able to visualise it. I get that problem. But the larger problem is that, i n order to get to that giant model that you’re complaining about, there are many, many years of labour, of people building in sticks-and-bricks models. How many airports have we designed in the history of human civilisation?

So, thinking about the fork we face – and I think we’re experiencing a ‘come to Jesus’ moment here – people are now seeing AI. As a result, they’re getting equal parts hopeful that it will suddenly, at a snap of the fingers, remove all the toil that they’re experiencing in building these bigger and bigger and more complicated models, and equal parts afraid that it will embody all the expertise that is in their heads, and will leave them out of a job!

Martyn Day: I can envisage a time where AI can design a building in detail, but I can’t see it happening in our lifetime. What are your thoughts?

Ian Keough: I don’t think that’s the goal. I don’t think that’s the goal of anybody out there – even the people who I think have the most interesting and compelling ideas around AI and architecture. But I do think there are a lot of people who have very uninteresting ideas around AI in architecture, and those involve things like using AI to generate renderings and stuff like that. It’s nifty to look at, but it’s so low value in terms of the larger story of what all this computing power could do for us.

At AEC Magazine, you’ve already written about experiments that we’ve conducted in terms of designing through our chat prompt/text-to-BIM capability. So, we took the summation of the five years of work that we have done on Hypar as a platform, the compute infrastructure and, when LLMs came along, Andrew Heumann on our team suggested it would be cool if we could see if we could map human natural language down into input parameters for our generative system.

We did that. We put it out there. And everybody got really, really excited. But we quickly realised the limitations of that system. It’s very, very hard to design anything real through a check prompt. It’s one thing to generate an image of a building. It’s another thing to generate a building. You’ll see in the history of Hypar that the creation of this new version of the product directly follows the ‘text-to-BIM thing’, because what the ‘text-to-BIM thing’ showed us is that we have this very powerful platform.

The new Hypar 2.0, which was released in September 2024, and more specifically, the layout suggestions capability, was our first nod towards AI-infused capabilities. The platform is all about seeing if we can make a design tool that’s a design tool first and foremost.

The problem with AI-generated rendering is you get what you get, and you can’t really change it, except for changing that prompt, and you’re totally out of control. What designers want is control. They want to be able to move quickly and to be able to control the design and understand the input parameters design. Hypar 2.0 is really about that. It’s about how you create a design tool and then lift all of this compute and seamlessly integrate it with the design experience, so that computation is not some other experience on top of your model.

Martyn Day: Historically, we have been used to seeing Hypar perform rapid conceptual modelling through scripting, generate building systems and be capable of multiple levels of detail to quickly model and then swap out to scale fidelity. The whole Hypar experience, looking at the website now, seems to be about space planning. Would you agree?

Ian Keough: That’s the head-scratcher for a lot of people when it comes to this new version. People who have seen me present on the work we did with DPR and other firms to make these incredibly detailed and sophisticated building systems are saying, “Wait, now you’re a space planning software now?”

That may seem like a little bit of a left turn. But the mission continues to enable anyone to build really richly detailed models from simple primitives without extra effort. We do this in the same way that we could take a low-resolution Revit wall and turn it into a fully clad DPR drywall layout, including all the fabrication instructions and the robotic layout instructions that go on the floor, and everything else. That capability still lives in Hypar, underneath the new interface.

What we are doing is getting back to software that solves real problems, again. This is a very gross simplification of what’s going on, but what problem does Revit actually solve? The answer is drawings, documentation. That’s the problem that Revit solves today and has solved since the beginning. What it does not solve is the problem of how to turn an Excel spreadsheet that represents a financial model into the plan for a hospital. It does not solve that at all. That is solved by human labour and human intellect. And right now, it’s solved in a very haphazard way, because the software doesn’t help you. It doesn’t offer you any affordances to help you do that. Everybody is largely either doing this as cockamamie-crazy, nested-family Lego blocks and jelly cubes in Revit, or trying to do it as just a bunch of coloured polygons in Bluebeam. That’s not how we’re utilising compute.

At the end of a design tool, it is still the architect’s experience and intellect that creates a building. What the design tool should do is remove all of the toil.

To give you an example of this, now that we’ve reached a point where users can use our software in a certain production context, to create these larger space plans, they’re starting to ask for the next layer of capabilities such as clearances as a semantic concept. This is the idea that, if I’m sitting at this desk, there should be a clearance in front of this desk, so that people have enough room to walk by. Sometimes, clearances are driven by code – so why has no piece of architectural design software in the last 20 years had a semantic notion of a clearance that you could either set specifically or derive from code? You might be able to write a checker in Solibri in the postdesign phase, but what about the designer at the point of creating the model?

Clearances are just one example. There are plenty of others, but the other impetus for a lot of what we’re doing right now is the fact that organisations like HOK have a vast storehouse of encoded design knowledge, in the form of all of the work that they’ve done in the past. Often, they cannot reuse this knowledge, except by way of hiring architects and transmitting this expertise from one person to the next, in a form that we have used for thousands of years – by storytelling, right?

What firms want is a way to capture that knowledge in the form of spaces, specific spaces, and all the stuff that’s in a space and the reasons for that stuff being there. And then they just want to transfer that knowledge from one project to another, whether it’s a healthcare project or any other kind of project that they’ve carried out before.

At the beginning of defining the next version of Hypar, when we started talking with architects about this problem, I was amazed by the cleverness of the architects. They’re actually finding solutions to do this with the software they have now. They build these giant, elaborate Revit models with hundreds of standard room types in them, and then they have people open those Revit models and copy and paste out stuff from the library.

I had one guy who referred to his model as ‘the Dewey Decimal System’. He had grids in Revit numbered in the Dewey Decimal System manner, such that he could insert new standards into this crazy grid system. And he referred to them by their grid locations.

In other words, architects have overcome the limitations that we’ve put in place in terms of software. But why isn’t it possible in Revit to select a room and save it as a standard, so the next time I put a room tag in that set exam room, such as a paediatric exam room, it just infills it with what I’ve done for the last ten projects.

To get back to your question about what the next generation looks like, I guess the simplest way to explain how we’re approaching it is that we’re picking a problem to solve that’s at the heart of designing buildings. It’s at the moment of creation, literally, of a building. We want to solve that problem and use software as a way to accelerate the designer, rather than a way to demonstrate that we can visualise larger models. That will come in time, but really, we want to use this vast computational resource that we have to undergird this sort of design, and make a great, snappy, fun design tool.

Martyn Day: Old BIM systems are oneway streets. They are about building a detailed model to produce drawings. But you have gone on record talking about tasks that need different levels of abstraction and multiple levels of scale, depending on the task. Can you explain how this functions in Hypar?

Ian Keough: You’ll notice in the new version of Hypar that there’s something called ‘bubble mode’. It’s a diagram mode for drawing spaces, but you’re drawing them in this kind of diagrammatic, ‘bubbly’ way.

That was an insight that we gleaned from spending literally hundreds of hours watching architects at the very early stage of designing buildings. They would use that way of communicating when they were doing departmental layout or whatever. They were hacking tools like Miro and other things, where they were having these conversations to do this stuff. But it was never at scale.

We were already thinking of this idea of being able to move them from lowlevel detail to a high level of detail without extra effort by means of leveraging compute. Now, in Hypar, and I’ll admit the bits are not totally connected yet in this idea, you’ll notice that people will start planning in this bubble mode, and then they’ll have conversations around bubble mode, at that level of detail.

Meanwhile, the software is already working behind the scenes, creating a network of rooms for them. And then they’ll perform the next step and use this clever stuff to intelligently lay out those rooms, the contents in the rooms. The next level of detail passed that will be connectors to other building systems, so let’s generate the building system. There’s this continuous thread that follows levels of detail from diagram to space – to spaces with equipment and furniture and to building systems.

Martyn Day: We have seen Hypar focus on conceptual work, space planning, fabrication-level modelling. Is the goal here to try and tackle every design phase?

Ian Keough: We’re marching there. The great thing about this is that there’s already value in what we offer. This is something that I think start-ups need to think about. You’re solving a problem, and if you want to make any money at all, that problem needs to have value at every point along the trajectory. That’s unless you raise a ton of capital, and say, ‘Ten years from now, we’ll have something that does everything.’

The reality is at day five, after you’ve built some software, and you put it in customers’ hands, that thing has to have value for them. The good news is that just in the way that we design buildings now, from low-level detail to high-level detail, there’s value in all those places along the design journey.

Why isn’t it possible in Revit to select a room and save it as a standard, so the next time I put a room tag in that set exam room, such as a paediatric exam room, it just infills it with what I’ve done for the last ten projects

The other thing that I think is going to happen, to achieve what we’ve been envisioning since the beginning of Hypar, is fully generated buildings. I do not believe in the idea that there’s this zerosum game that we’re all playing, where somebody’s going to build the one thing that ‘owns the universe’.

This is a popular construct in people’s minds, because they love this notion of somebody coming along and slaying the dragon of Revit in some way, and replacing it with another dragon.

What’s going to happen is, in the same way that we see with massively connected systems of apps on your phone and on the internet, these things are going to talk to each other. It’s quite possible that the API of the future for generating electrical systems is going to be owned by a developer like Augmenta (www.augmenta.ai). And since we’re allowing people to layout space in a very agile way, Hypar plugs into that and asks the user, ‘Would you like this app to asynchronously generate a system for you?’

Now, it might be that, over Hypar’s lifetime, there will be real value in us building those things as well, because most of the work that we’re doing right now is really about the tactility of the experience. So it might be that, to achieve the experience that we want, we have to be the ones who own the generation of those systems as well, but I can’t say yet whether or not that’s the case.

Everything we’re doing right now in terms of the new application is around just building that design experience. What we do in the next six months to one year, vis-à-vis how we connect back into functions that are on the platform and start to expose that capability, I can’t speculate right now.

What we need to do is land this thing in the market and then get enough people interested in using it, so that it starts to take hold. Some of the challenge in doing that is what you alluded to earlier, which is that people are trying to pigeon-hole you. They’ll ask, ‘Are you trying to kill Revit?’, or, ‘Are you trying to kill this part of the process that I currently do in Revit?’ That’s a challenge for all start-ups.

The decision that we made to rebuild the UI is about the long-term vision we have for Hypar. That vision has always been to put the world’s building expertise in the hands of everyone, everywhere. And if you think about that longterm vision, everybody will have access to the world’s building expertise. But how do they access it? If it’s through an interface that only the Dynamo and Grasshopper script kids can use or want to use, then we will not have fulfilled our vision.

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In Buro Happold’s structural engineering team, we’re constantly working on unique and challenging projects, from towering skyscrapers to expansive stadiums, intricate museums to impressive bridges.

Our approach is all about exploring multiple options, conducting detailed analyses, and generating 3D and BIM models to bring these projects to life. But this process comes with the major challenge of interoperability – the ability of different systems to exchange information.

Since we collaborate with multiple disciplines and design teams from all over the world, we regularly deal with data from various sources and formats, which can be a real challenge to manage.

The AEC industry often deals with this by creating ad-hoc tools as and when the need arises (such as complex spreadsheets or macros). But these tools often end up being one-offs, used by only a small group so we end up reinventing the wheel again and again.

This is where the BHoM (Buildings and Habitats object Model) comes into play, a powerful open-source collaborative computational development project for the built environment supported by Buro Happold.

BHoM helps improve collaboration, foster standardisation and develop advanced computational workflows. Thanks to its central common language, it makes it possible to interoperate between many different programs.

Instead of creating translators between every possible combination of software applications, we just need to write one single translator between BHoM and a target software, to then connect to all the others.

The solution: The BHoM

The BHoM consists of a collection of schemas, functionalities and conversions with the following three main characteristics:

• It attempts to unify the “shape” of the data

• It is crafted as software-agnostic

• It is open source so that everyone can contribute and use it

Currently, the BHoM has over 1,200 object models with an extendable data dictionary and adapters to over 30 different software packages.

With the BHoM, we’ve refined and enhanced our approach to structural design.

Once the architectural model is received, using the BHoM we can quickly and precisely build several Finite Element Analysis (FEA) structural models for conducting structural analyses.

It’s possible to clean and rationalise the original geometries for specific purposes and assign/update attributes to all objects based on the results of both design and coordination with other disciplines.

Finally, the BIM model of the structure can be generated in an algorithmic manner.

BHoM in practice

It’s often thought that computational and parametric design is only applicable to the very early stage of a project that relies on very complex geometry.

The reality is, computational design is greatly beneficial at every stage: from the conceptual feasibility study to the detailed design of steel connections.

At Buro Happold, we use the BHoM to help us address multiple stages throughout a project, as demonstrated in the following case study examples which focus on the re-development of a desalination plant in Saudi Arabia into a huge museum.

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Modelling the existing and the new

Let’s see how a computational workflow applies to the modelling and analysis of existing structures making use of the BHoM.

For the Saudi Arabian project, all we had was a set of scanned PDF drawings of the existing structures.

Within a couple of months, we had to build accurate Finite Element Models for each of them and run several feasibility studies against the new proposed loadings.

A parametric approach was vital. Therefore, we developed a computational workflow that allowed us to create the geometric models of all the built assets in Rhino via Grasshopper by tracing the PDF drawings, assigning them with metadata and pushing them via BHoM into Robot to carry out preliminary analyses and design checks.

Of course, there’s no need to mention how much time and effort this approach has saved us compared to a more traditional workflow.

Moving on to the next stage of the project, we needed to test very quickly many different options for the proposed structures, by modifying grids, floor heights, beams and column arrangements, as well as playing with the geometry of arched trusses and trussed mega-portals.

Again, going for a computational approach was the only way to face the challenge and we developed a large-scale algorithm in Grasshopper.

By pulling data from a live database in Excel and making use of an in-house library of clusters and textual scripts, this algorithm was able to leverage the capabilities of the BHoM to model the building parametrically in Rhino, push it to Robot for the FEA and finally generate the BIM model in Revit – all in a single parametric workflow.

Managing data flow: BIM – FEA

As we move into later stages of the project, the more we can see how computational workflows are not only beneficial for geometry generation but also for data management and design calculations.

At Stage 03 and 04 we needed to be able to transfer and modify very quickly all the huge sets of metadata assigned to any asset within our BIM models while being able to test them on a design perspective in Finite Element Software.

Again, we developed an algorithm in Grasshopper leveraging the BHoM to allow for this circular data flow from BIM to analysis software – Revit and ETABS in this instance.

This made it possible to test and update all our models quickly and precisely, notwithstanding the sheer amount of data involved.

Interdisciplinary coordination

As usual, when moving forward in the project, coordination with MEP engineers starts to ramp up and when structures are big and complex, it becomes even more difficult. The challenge we had to face was intimidating. We had eight concrete cores, 45m tall, more than 9,000 Mechanical, Electrical and Plumbing (MEP) assets for the building and around 1,500 builderswork openings to be provided in the core walls to allow them to pass through.

On top of this, we had the need to specify openings of different sizes depending on different requirements based on the type of MEP asset, as well as the need to group and cluster openings based on their relative distance and other design criteria.

Again, a high level of complexity and a huge amount of data to deal with. Indeed, a computational approach was needed.

Using Grasshopper, BHoM and Rhino. Inside Revit, we developed an algorithm, graphically represented below.

Through grouping operations, model laundry algorithms and the parametric modelling of the builderswork openings, we were able to generate parametrically the BIM model of the cores provided with the required builderswork penetrations.

In parallel with this, the algorithm also generated the corresponding FE model of the core walls, so the structural feasibility of the penetrations could be checked before incorporating them in Revit.

The algorithm detected the intersections between pipes and walls, then generated openings around each intersection of different size and colour depending on different input criteria. Then, using a fine-tuned grouping algorithm, it clustered and rationalised them into bigger openings, wrapping all of them together based on user-input criteria.

Finally, after testing the openings in the Finite Element software, the algorithm pushed them into Revit as Wall Hosted Families and a live connection between the Rhino and the Revit environment streamlined any update process in parallel.

Producing large data sets

Moving even further into detailed design, the amount of data to deal with on a project of such scale becomes more and more overwhelming.

This is what we had to face when dealing with the design of the connections. Although the design was subcontracted to another office, we faced the challenge of providing all the connection design forces in a consistent and comprehensive format, both in textual and graphical contexts.

Indeed, this is not an easy task, especially when dealing with around 35,000 connections, 60 load combinations, 2,000 different frame inclinations, six design forces per connection and spanning over three different finite element software packages (ETABS, Robot, and Oasys GSA).

We had to deal with 12.6 million pieces of data and we had to do it very quickly, being able to update them on the fly. Again, a computational workflow was required.

Via Grasshopper and the BHoM, we developed an algorithm to extract, post-process and format the connection forces from the Finite Element models of all the assets of the project, serialise them in JSON, save them in properly formatted Excel files and show them graphically in corresponding Rhino 3D models via tagging and attributes assignment.

All this information was sent out for the design to be carried out by other parties.

Conclusions

Applying a specialised approach, relying on algorithmic methodology and leveraging state-of-the art computational tools, such as the BHoM, enable us, at Buro Happold, to deliver comprehensive and advanced structural solutions, ensuring efficiency, sustainability, and optimal performance across all the stages of the project.

Resources

[1] BHoM Documentation (2024)

[2] LOMBARDI, Alessio, (2023), Interoperability Challenges. Exploring Trends, Patterns, Practices and Possible Futures for Enhanced Collaboration and Efficiency in the AEC Industry, in, London, UK.

[3] ELSHANI, Diellza, STAAB, Steffen, WORTMANN, Thomas (2022), Towards Better Co-Design with Disciplinary Ontologies: Review and Evaluation of Data Interoperability in the AEC Industry, in LDAC 2022: 10th Linked Data in Architecture and Construction Workshop, Hersonissos, Greece.

The post BHoM – addressing the interoperability challenge appeared first on AEC Magazine.

Graphisoft is part of the Nemetschek Group, which comprises thirteen AEC related brands, three of which are BIM modellers.

While the brands are individually strong, it’s taken a while for Nemetschek to realise that together they can represent a connected ecosystem solution for the whole AEC industry. This has resulted in a change of strategy, with growing interconnected workflows and now brand separated teams working together on company-wide technology innovation.

Graphisoft, which has Archicad as its flagship BIM platform, is set to become a major beneficiary of Nemetschek’s new pan-brand accelerated development.

I’ve always felt it was in Graphisoft’s DNA to be a ‘secret squirrel’ when it came to talking about new technology, beyond the current release. On this trip to the HQ in Budapest, I finally realised why.

Jump to section on AI / ML and legacy code

Unlike most other developers in the industry, which have already transitioned to subscription-based sales, Graphisoft has been some way behind the curve into moving into that business model. This has meant that every release needed to perform and appeal to customers. The company’s marketing and focus needed to be on the latest release and the benefits of that yearly update, not on features that were still years away. The net result was we only got only vague insight into the future roadmap.

In 2023, Graphisoft took a significant step forward by beginning to circulate comprehensive product roadmaps for Archicad, BIMcloud, BIMx, and DDScad (MEP – mechanical, electrical, plumbing). These roadmaps categorised the status of various features as ‘under research’, ‘in progress’, ‘coming soon’, and ‘delivered’.

Graphisoft also introduced a Technology Preview Program, to share and experiment with forthcoming features, so users can have input on the development and evolution of Archicad.

This year, we were invited to play with a range of augmented reality (AR) and artificial intelligence (AI) technologies that are still in the experimental phase, sparking discussions about the transformative potential of AI within the AEC sector.

The transition to a subscription model has evidently fostered a more open dialogue from Graphisoft’s product managers, who now appear more inclined to speculate on the future trajectory and capabilities of BIM 2.0, as well as the broader applications of technology across the AEC market.

This is indeed an opportune moment for AEC technology developers, as the landscape is ripe with possibilities for the creation of innovative solutions. The shift towards a subscription model potentially enhances customer engagement, and also allows for a more agile response to market demands and technological advancements. As Graphisoft continues to evolve and adapt to the changing dynamics of the industry, the potential for collaboration and integration among the various Nemetschek brands will likely yield significant benefits for architects and other professionals within the AEC sector.

The strategic realignment within the Nemetschek Group, coupled with Graphisoft’s newfound transparency regarding its product roadmap, heralds an exciting chapter for the company. Graphisoft is fostering a future where technology not only enhances customer’s design capabilities but also fosters a more interconnected and collaborative approach to building and construction. The journey ahead promises to be filled with opportunities for growth, innovation, and the advancement of our profession.

Graphisoft 2024 lineup

Graphisoft’s core product, Archicad, is now in its 28^th revision. For an opener, this year’s update offers a substantial 30% improvement in performance and the development team is impressed with Apple’s new ‘system on a chip’ silicon-based systems. Asked if Graphisoft would support the ARM version of Windows, it seemed the company had the software compilers and were working towards future support for Archicad on ARM.

There are the usual UI tweaks, like a new home page start point on launch. Improved model exchange support through IFC, RFA and RVT for Revit. New support for Information Delivery Specification (IDS) format – a protocol for enabling the construction sector to establish and validate BIM specifications, leading to the automation of quality assurance checks and categorisation. Graphisoft has also added support for BIM Collaboration Format (BCF) 3.0.

Advanced distance arrows / guides give excellent measurement feedback when moving and positioning to relative geometry. Roof openings can be created quicker and more consistently in an expanded Opening Tool. There’s a flexible global library,

‘Keynotes’ debut in Archicad 28, to enable a database-driven documentation system that integrates specifications and legends. This is streamlined by automating annotations, eliminating the manual workarounds that were previously required for maintaining consistency across project documentation sets.

Architects face a common challenge when presenting multiple design concepts to clients. The process typically requires many hours to manually create different versions of the same project. Archicad’s new Design Options delivers a new workflow within a single project file. Users can create variations for a whole building or focus just on specific areas. Different façade treatments or interior layouts are good examples. These alternatives work independently, allowing for smooth 2D and 3D views and are compatible with annotations, sections, and elevations. It’s possible to merge, duplicate, or rearrange options within the workflow. This is a very powerful technique, and one that is used in the more advanced mechanical CAD tools. It reduces the need to start multiple project files to develop different options.

Archicad has improved Rhino-Grasshopper connection which supports the latest version of Rhino. It offers increased speed with a built-in parametric hotlink capability, where Archicad geometry becomes hotlinks in Grasshopper – enabling capabilities such as laying out of buildings. Beam and column support has also been extended.

The Archicad AI Visualizer, which was originally desktop-based, now runs in the cloud, which means it doesn’t have to be installed, and users don’t need powerful local GPUs. The software takes Archicad designs and uses AI to generate photorealistic images based on text prompts. To change a material, the user simply alters the text prompt and the image is regenerated.

Graphisoft still has strong links with Chaos Group and its Enscape renderer too for more traditional viz output. The new Chaos AI Enhancer is also accessible to Archicad users, delivering exceptionally smooth daylight shadows. In fact, there are no shortages of rendering tech, as there is also Graphisoft’s stablemate, Maxon, which offers Redshift and Cinema 4D too.

There’s a new LCA (Life Cycle Assessment) analysis capability provided by third party developer One Click, to give fast feedback on lifecycle assessments and life cycle costing of their various design options.

Point cloud has been improved with the inclusion of a ‘Lite’ layer of functionality from third-party developer, BIMmTool, aimed at assisting renovation and refurbishment projects within Archicad. It means Archicad can now handle larger data sets, leading to faster workflow. To maintain speed while handling dense point clouds, the software displays distant point clouds at lower resolution.

Users that want even more power can upgrade to the full version from BIMmTool, which supports the direct import of common point cloud formats, such as Leica, Faro, Riegl and others. Thanks to multiprocessing support and sampling, BIMmTool supports large scan projects easily, while the Pointcloud switcher allows precise control over point cloud display in Archicad views. The full version also provides a suite of tools for modelling off point clouds, as well as analysis for deviation of reality vs BIM.

Archicad also offers improved integration with its sister Nemetschek brands, including Solibri for design checking / validation and Bluebeam for PDF-based collaboration.

DDScad for MEP

In June 2022, Graphisoft merged with its sister company, DDScad, adding the mechanical, electrical and plumbing (MEP) expertise of DDS to its BIM platform. This was a major step forward to producing a like for like multi-disciplinary BIM system to compete with Revit. Unfortunately, the Archicad brand obfuscates the fact that the BIM tool is more than just about architecture. It now offers MEP, as well as structural, through other Nemetschek brands. The integration work is significant and Graphisoft maintains DDScad as a standalone product for its significant European installed base.

DDScad is an advanced MEP solution which designs ductwork specifications with flow rate optimisation, with an easy-to-use browser-based system to visualise routes. It also delivers streamlined cable tray and piping workflows and real-time size optimisation, based on flow requirements.

Customers want to buy into a technology firm that has a vision and can show velocity of innovation. BIM software needs to deliver considerably more productivity to users and I don’t get the feeling Graphisoft is resting on its laurels

New for this year, there are enhancements to photovoltaic system design, busbar trunking design and modelling and generic workflow improvements, specifically allowing electrical engineers and architects to exchange models easily. Lighting design also gets updated with an improved DIAlux-evo electrical connection, for planning, calculation and visualisation of lighting. Dial is the company which develops DIAlux-evo in Germany and is available free of charge in 26 languages.

BIMcloud for collaboration

Graphisoft’s cloud-based data platform, BIMcloud, for architects, engineers, and constructors enables real-time collaboration on projects. BIMcloud has 13 regional data centres worldwide through Graphisoft’s partnership with Google Cloud Platform.

While BIMcloud was slow to gain adoption, like all cloud collaboration tools it has started to get traction post COVID, with remote working and distributed teams growing and fears of cloud subsiding. This year, multi-factor authentication is being added to build in more security and Graphisoft is offering it as a turnkey service.

BIMx for presentations

Graphisoft’s collaboration and presentation tool, BIMx, now supports antialiasing, leading to better quality model edge definition. The new release also provides feature unification across all supported platforms, mobile, web and desktop – macOS, Safari, Windows, Firefox, IOS, Android, Chrome and others.

There are more controls to filter visibility options to enable the viewing of design options, renovations and structure. This is easy to use and an incredibly effective visual aid when showing clients.

BIMx now supports the Apple Vision Pro, although since the announcement Apple has paused production of the high-resolution headset, and rumours are that work on the second generation has been suspended. We hope this work progresses once Apple figures out the way ahead.

Autodesk Nemetschek API deal

In April this year, Nemetschek and Autodesk signed an agreement to advance and open interoperable workflows between their products. This was great news and included the exchange of software, API developer access and for Nemetschek to access Autodesk Platform Services APS (formerly Forge). This lowered the bar of entry for Nemetschek to connect Bluebeam, BIMcloud and BIMplus to Autodesk Forma, Autodesk Fusion, Autodesk Construction Cloud (ACC) and design products.

While it’s still early days, it was interesting to discuss the opportunities this might present to Graphisoft. It’s possible that Graphisoft could develop tools that rely on APS, which essentially is Autodesk’s modularised services, like viewing tools, file translation, design automation, and even access to the new granular data and geometry feeds from Autodesk Docs, which were launched this year.

Co-opetition is a strange new world as AEC firms open up, but it was encouraging to hear that Graphisoft’s team were aiming to make use of this agreement in building connected AEC workflows, trying to remove the historic silos.

Architecture tour

During the CAD conference in Budapest, Graphisoft organised a notable architectural excursion to refresh our minds away from BIM software discussions. The destination was Napraforgó utca (Sunflower Street), situated near the intriguingly named ‘Devil’s Trench’, a significant modernist development. The estate comprises 22 structures commissioned for the 1930 International Architecture Congress.

The project featured 18 distinguished architects, including Henrik Böhm, József Fischer, Alfréd Hajós, Ármin Hegedűs, Lajos Kozma, and Farkas Molnár. This remarkable collection of Bauhaus-style modernist villas remains predominantly residential, with one property, formerly the house of an opera singer, on Bajza utca functioning as a public museum.

From seeing the original photographs, the estate was clearly built near the northern outskirts of the Buda side of the city, but since then the urban sprawl of a nation’s capital city has somewhat swallowed it up.  If you are in Budapest, and a fan of modernism, it’s well worth the trip.

Conclusion

It’s incredible how broad the Graphisoft software and services portfolio has grown – now catering to architects, electrical engineers, mechanical engineers, sustainability experts, construction specialists, and arch viz experts, extending out to surveyors, and then customers with BIMx. With each release, the span of software development grows.

This release also sees Graphisoft do something else it has not been so good at in the past – working with third parties. Bringing in BIMmTool and One Click LCA, this enables ‘lite’ layers of functionality to be integrated, exposing their customers to third party tools which build on the functionality provided, adding extra levels and layers to Archicad’s capabilities.

Graphisoft will convert to selling only new subscription licences from 2026 which is a sure-fire way to rile up the base as inevitably the cost of ownership goes up. This can be assuaged to a degree by offering more value and much longer low-cost subscription deals. The messy bit with subscription is time and price inflation – compound inflation eventually adds up to make an expensive solution and software providers fall foul with even their in-house product champions. However, maybe in ten years, AI will mean that eventual seat sales of software may have to make way for a new value-based pricing model.

It’s interesting how attitudes are changing in the software development community. While I am not a massive fan of subscription for many reasons, there is one positive thing that is coming from it and that is open product development. It’s no longer about selling the next release and trying to get as much of the base to buy in to this year’s features. We have moved decisively to open development, where customers get a much longer view of where software development teams have been going.

Customers want to buy into a technology firm that has a vision and can show velocity of innovation. BIM software needs to deliver considerably more productivity to users and for the first time in 20 plus years there are some new kids on the block. I don’t get the feeling Graphisoft is resting on its laurels, and that sentiment now spans the whole Nemetschek Group.

AI / ML and legacy code

In a CAD world full of artificial intelligence (AI) and machine learning (ML), it will be obvious to see that there is not a lot of innovative AI shipping with Archicad currently. The most notable component was this year’s inclusion of the AI Visualiser tool, but this was released in a world of exploding AI visualisation tools, which basically all do the same thing – harness ChatGPT with and AI renderer (Stable Diffusion), using BIM geometry as the seed. EvolveLab was first to market with Veras in December 2022.

AI Visualiser was a rapidly developed tool, which started off as an in-house AI experiment that progressed rapidly and gave impressive results so the decision was made to release it.

I talked with Màrton Kiss, Graphisoft CPO and Sylwester Pawluk, senior director of product management about AI, pre-Archicad 28 launch, back in September 2024. At the event we had plenty more time to further our conversations and talk about the future application of AI.

Both Nemetschek and its brands are all active in developing AI features and new products and this is being managed centrally by an AI group at Nemetschek. We will see discreet AI applied within Nemetschek’s brands, jointly developed technology between different brand developers and possibly new products emerge in their own right from Nemetsheck.

The Graphisoft product team is certainly not short on ideas. Our conversations ranged from automated detail modelling, 2D to 3D BIM, 3D BIM to 2D drawings, mass transcription of architectural catalogues to 3D BIM components, scan a room direct to AR, auto routing of MEP, auto modelling of MEP, automatic structural design based on architectural BIM modelling, modelling with intelligent massing rooms which automatically generate Level 300 detail models.

All of these topics are being considered or are in some form of being actively researched. Depending on how well they go, they could end up on the Archicad roadmap or be taken up within the Nemetschek Group as they could benefit all brands.

It is of course possible that the AI technology is deemed so game changing that it is kept in stealth until unleashed. With the industry seeing so many cloud-based ‘wannaBIM’ start-ups and the future battle for the AEC design authoring tool for the next decade.

Talking with Pawluk, who was previously at GE Healthcare / GE Avionics and Google, you can’t help but get excited about the potential applications for AI within the whole product suite. But with this new change of pace in development, it’s important not to break the existing product and still deliver reliable code.

We know internally the company has engaged in a multi-year project to rewrite and modernise Archicad’s core underlying code, removing the legacy. Called the Adaptive-Hybrid Framework (AHF), that program is being led by Zsolt Kerecsen, Graphisoft’s CTO. Essentially Archicad’s core has to become modular, extensible, support cloud and desktop as native, and be ready to deliver quarterly feature updates and AI, ML / neural net-based capabilities.  In fact, some of this work has already been done as two of this year’s features utilised the AHF – Design Options and Keynotes.

The BIM competitive landscape

With so much happening in the world of BIM, with all the upstarts and new developers, it’s worth spending a little time understanding the competitive landscape.

Autodesk is the 800 lb industry gorilla and Revit is its global leading BIM tool for others to beat. However, since the ‘Open letters to Autodesk’ (A tale of two open letters), Autodesk was pressed to go on record to say that there will be no new ‘next generation’ desktop version of Revit. The company has since launched Autodesk Forma, a next generation cloud-based AEC platform, on which it plans to develop industry tools, spanning design, simulation and fabrication workflows.

Autodesk is currently still developing new features for Revit but these seem to be ones which don’t need the developers to go into the guts of the programme for major software architectural rewrites.

The challenge is to somehow build a bridge between Revit and Forma and eventually deliver next generation BIM with a cloud-based datacentric workflow, incorporating desktop Revit and somehow reversing it into cloud-centric workflows and tools. This process could take anywhere from 5 to 10 years to complete.

Nemetschek has three core BIM brands – Archicad, Allplan and Vectorworks. None of these tools are cloud-first, all are desktop with cloud extensions, but increasingly cloud-connected. With Autodesk choosing a difficult path of spinning multiple plates, maintaining the old version and workflow while fleshing out the new, there is an opportunity for Archicad, with focused development, to become a real multidisciplinary competitor for Revit. However, legacy software is sticky and proprietary formats and hard-earned skillsets inhibit momentum to change.

But looking ahead, in 2027 / 2028 Autodesk’s two-for-one perpetual to subscription deal runs out. Many firms will be facing paying around £10,000 per seat for their next 3-year deal for half their seats (which they have enjoyed for gratis for the last eight years) to be renewed as an AEC collections. Added to this, their current deals on historic seats, which were heavily discounted through dealers will also need renewing. And now Autodesk has taken over the sales process, customers won’t have access to dealers who previously cut their own margins for a sale.

The net result will be a potential combined double whammy – buying 50% new licences together with the cost increase to existing licences. This is, of course, assuming that Autodesk won’t try and do something to soften the blow, which it may well do as there are three years to go.

Of course, all of this may present a huge opportunity for competitive AEC software firms like Graphisoft that can provide an alternative means to model and document their projects with significant budget savings. Customers will face some tough budgetary and tech stack decisions over the next five years. It’s safe to say that even with so many start-ups aiming for a slice of the BIM market, Revit and Archicad will still offer the deepest, most mature BIM feature sets.

Archicad Collaborate

In March 2023, Graphisoft offered the Archicad Collaborate subscription-based, value bundle for power users. This combined Archicad, BIMcloud as a service (no IT overhead), Graphisoft Learn courses and materials, Redshift renderer by Maxon, PARAM-O object design tool, Library Part Maker and Python API for a single discount subscription fee.

The 2024 Collaborate offering is intended to replace its Software Service Agreement (SSA) / Forward (FWD) subscription program, and includes Archicad, BIMx Pro, BIMx model transfers (private storage, password protection, embedding), Redshift, Surface Catalogue (500+), Pythion API, PARAM-O, Library Part Maker, Technical support, Graphisoft Learn, Emergency licences (replace lost or stolen keys), and Archicad Design Checker (powered by Solibri).

Collaborate is essentially the full Graphisoft tech stack and enables centralised project management with real time synchronisation across all connected devices and team members. There’s built in version control, logging and tracking all iterations, and there are quality checking and conflict resolution tools. Designs can be shared with team members and clients, sent for markup and annotation. Tasks can be assigned and tracked. Access is controlled based on roles.

BIM data is stored securely in the cloud and distributed across multiple platforms. The cloud server provides automatic back-up and utilises the power of the cloud, freeing up local machines for other tasks.

From 2026 Archicad will be available for purchase only via subscription. The bundle options appear to be Archicad Collaborate now or Archicad Studio in 2025. We suspect the individual products will remain as subscription items on the menu.

Main image: Archicad’s new Design Options allows users to create variations of a building within a single file

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Generative Design (GD) is a technology-driven approach to optioneering designs by using algorithms to explore a vast number of possible solutions based on defined constraints and goals.

By inputting parameters such as unit mix, desired building heights, spatial layout requirements, and performance criteria, GD software generates numerous design options. This allows designers to evaluate and select the most optimal solution. Instead of manually crafting each option, designers can review a wide range of solutions, filter and adapt them further, enabling new creativity, or simply getting optimal answers faster for clients.

The expanding landscape

As the popularity of GD has increased, so has the number of available tools. This is both exciting and overwhelming.

It is clear that GD software developers have identified early stage feasibility as an area that GD can be applied to effectively. The software should help designers make decisions faster, using data to confirm project viability, and communicate options more effectively than traditional CAD/BIM authoring tools. However, this presents a challenge for professionals to learn which one is best for them, manage licence costs, and then understand how to integrate them into their own tech stack effectively.

Breaking down barriers to adoption

Despite the promise of GD, there are still notable barriers to adoption for many AEC professionals.

1. Time to Learn: GD tools have opened the door for many users to explore data driven design but within a proprietary format, without a steep technical learning curve of visual scripting. GD tools still require time and effort to master, which can pose challenges for busy professionals.

2. Cost: Licensing fees vary from free trials and low introduction fees, to expensive Software as a Service (SaaS) models. When added to the growing list of software required for AEC workflows, this is an additional cost that puts pressure on companies already struggling to manage cash flow. While GD tools may offer long-term savings by accelerating workflows, the recurring monthly costs can deter adoption.

3. Workflow Integration: Not all GD tools integrate smoothly with existing software ecosystems, which can create friction. Users often rely on a core suite of tools, and if a GD solution disrupts these workflows, it may slow productivity. Ensuring compatibility and ease of integration with a company’s preferred BIM authoring software is crucial for broader adoption.

4. Functionality: These tools are new and are continually expanding the range of functionality they offer. However, no single tool has managed to provide the comprehensive design functionality that meets all user needs. A careful study of requirements is required to make sure the software does what is needed.

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A tool for clients and developers

An emerging trend among ConTech software companies is a shift of focus from selling to architects to targeting developers and clients, who may wish to understand site potential before hiring a design team. Tools that automate site analysis and feasibility studies allow them to receive insights into purchases at a much earlier stage. This has an impact on the architect’s traditional role particularly in the early-stage design process.

This shift presents both opportunities and challenges. While it may reduce architects’ involvement in early feasibility studies for some clients, it also creates an opportunity for architects to use these tools for clients who seek deeper data insights but prefer not to use the tools directly.

By accessing data and insights generated by GD solutions quickly and easily, architects could provide value faster, creating more refined designs that align with clients’ initial requirements. This approach suggests architects should adapt, adopt a solution, and potentially provide new services for clients. GD users could enhance their role in delivering data-driven projects with this approach.

Risks and challenges

While the potential benefits of GD are clear, there are some risks that users should consider carefully:

1. Quality vs. quantity: The ability to generate a multitude of designs quickly can be both a blessing and a curse. With too many options, architects may face decision fatigue, or find themselves sacrificing quality for speed.

2. Cost and learning barriers: For many architects, especially smaller firms, the cost and time to learn required to adopt these tools remains a hurdle.

3. Dependence on technology: Over-reliance on GD tools can sometimes overlook essential design considerations that come from experience, intuition, and human creativity. Designers who depend heavily on GD software should augment this with a hands-on design approach that allows for unique, site-specific insights.

How to assess GD tools

With the growing variety of GD tools, users need a consistent way to evaluate software that goes beyond features and marketing hype. This is why at ADDD, we have developed an assessment criteria, based on the Future AEC Software Specification (FASS).

The aim is to enable a consistent approach to reviewing software so different tools can be compared to each other. This assessment methodology was presented at AEC Magazine’s NXT DEV conference and has now been used to assess four Clash Detection and Issue Management software.

The outcome is a quantitative framework of questions designed to bring clarity and consistency to software evaluation. When combined with a qualitative approach, where users can communicate their findings and thoughts, a report can be produced that supports the AEC industry to have a consistent way to assess software.

GD tools will undoubtedly disrupt early-stage ‘‘ design within the AEC industry, offering powerful capabilities that allow users to explore, create, and iterate with unprecedented speed

The assessment provides a structured, objective way to assess GD tools on critical factors such as ‘Designing in Context & Scale’, ‘User Experience’, ‘Modelling Capabilities’, and the other criteria from the FASS. By applying the FASS criteria, users can identify tools that align with their needs, budget, and project demands, allowing them to make decisions that suit their requirements. This approach not only simplifies the selection process but also ensures that chosen tools meet the demands of modern AEC workflows.

I am also advocating for Best For …’ results, rather than one software being better than another, as this is too simplistic. In a landscape flooded with options, having a consistent assessment method like the FASS can be invaluable, helping users to navigate to the appropriate GD tool that is ‘Best For Their Needs’.

N.B. Information about the assessment criteria can be found here . ADDD also welcomes feedback on how software is assessed.

Charting a way forward

GD tools will undoubtedly disrupt earlystage design within the AEC industry, offering powerful capabilities that allow users to explore, create, and iterate with unprecedented speed. This will require adaptability, a consistent structure to assessing software, and a way to help strategic decision-making within organisations.

As GD options continue to search for their specific niche, AEC professionals have the opportunity to explore and embrace these tools as allies in their work, with the opportunity to lead to better client outcomes. With resources like the FASS Assessment, users can assess, compare, and select the solution that aligns with their organisational goals, empowering them to succeed in a digital future that balances creativity with technology.

Learn more @ NXT DEV

At NXT DEV in June Allister Lewis presented ADDD’s assessment methodology based on the Future AEC Software Specification. See the presentation here.

NXT DEV 2025 will be held at the Queen Elizabeth II Centre in London on 12 June 2025

The post Navigating the boom in generative design software appeared first on AEC Magazine.

Established in 2005, Dalux is a Danish software firm which has created a digital platform for almost everything outside of BIM authoring tools. It focusses on information management, design management, model validation, tendering, site inspections, and snagging, for construction firms, developers and consultants. Dalux’s software expands through the lifecycle to data handover and facilities management. The company is also scaling up into infrastructure and GIS.

Dalux started off creating what it claims to be the fastest BIM model viewing tool, being first to apply games technology to BIM geometry, an early entry into what is now the Common Data Environment (CDE) market. From that initial product Dalux has built a whole platform around its centralised data model, expanding to mobile and augmented reality.

Dalux now has a global user base of over 1 million professionals across 147 countries. Despite its scope and reach, the company is very much headquartered in Copenhagen, Denmark, which is the centre of operations and development.

The company has an annual user meeting, the Dalux Summit, which is hosted in Copenhagen. This year AEC Magazine attended to delve deeper into the products and the community. With over 1,800 attendees, the scale was much larger than we anticipated and the whole vibe was a unique experience.

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Dalux feels like a family business and the dialogue and interactions between customers and the team gave the distinct impression that development of features and capabilities was a much more interactive process than at other software companies. Dalux has ‘Hygge’, a Danish word that roughly translates to ‘cosiness.’

The family business is run by two brothers, Torben and Bent Dalgaard. Torben is the CEO and Bent is the CTO. In their morning address to attendees, one slide caught the zeitgeist perfectly – the brothers reassured the audience that Dalux is an independent software firm, that has no loans, no investors and is owned by Bent & Torben.

While many 19-year old firms that have been growing 60% annually for almost a decade would be wafting share value, revenue or profit as an essential part of their mainstage moment, they opted to reassure customers that, unlike firms with shareholders that are repeat plunderers of their customers’ design technology budgets, Dalux is not in that game. There are very few AEC software companies with this attitude that come to mind – the most notable others being McNeel (Rhino) and Qonic.

The Dalux product family

At the moment, Dalux offers nine products, which it has grouped in information management, onsite management and facility management, with almost half of its brands in on-site management.

BIM Viewer is free and works on desktop and mobile. It supports native BIM, IFC and drawings, with a range of free plug-ins for Revit, Solibri, Archicad, Navisworks and Tekla. It offers a suite of tools including measure, filter, properties, and make sections. Comments can be added, clashes from Solibri and Navisworks can be imported. We suspect that this is the gateway drug to the Dalux ecosystem!

Box is the core collaboration and CDE platform that delivers BIM geometry and data to collaborating project teams. It is accessed via the web browsers of supported mobile devices (iOS or Android).

Having extracted the data from the BIM authoring tool, Box centralises all the project information in managed folders for design and construction teams to view, review and approve 2D and 3D data with individual team controls. Additionally, Box offers the ‘always requested’ clash detection, for both hard and soft clashes, as well as perform other geometry checks, such as point clouds from as-built.

Dalux Box Sync will download folders and files between the web and a local computer. It will also upload any files you want or have changed, making them available to other project participants.

Field is the product for quality control, health and safety, snagging/punch list and on-site reality capture. It brings the latest drawings to site and assists in scheduling and managing site inspections with customisable checklists. While onsite observation/ snags, health and safety reports can be quickly created and documented with the phone’s camera, the system is smart enough to know where in the site you are located – time and floor. There are workflow tools to trigger actions to those who need to resolve remedial work. The reports are accessible to project workers and issues clearly identified on the latest drawings.

Field Basic is a free punch list tool that supports drawings and BIM models and enables tasks, collaboration amongst defined groups, and sign-off.

Field Sitewalk enables the quick capture of a site using video from a helmet-mounted 360 degree camera. The video frames make photographing the site effortless. These are automatically mapped in the Dalux system. Teams back in the office can use the system to see the current state of construction and even compare the site against the BIM model to see if the work is on track. The system offers some very clever registration between the rooms captured and generating the same view from the BIM geometry.

Infrafield is Dalux spreading its wings into the world of projects that span tens of kilometres, rather than metres with individual buildings. Given Dalux’s client list, we can well understand how Infrastructure became inevitable.

Infrafield required a new modelling engine technology to provide the expansive co-ordinate system. It supports 2D and 3D, Google Maps 3D tiles, drawings, GIS layers, terrain layers, and point clouds. Like ‘Field’ it can be used to track progress and capture issues. Users can create sections and cuts, measurements. It is seamlessly integrated into the Dalux ecosystem, so infrastructure models can be shared.

FM – facilities management – is probably another no brainer for following the design and construction data, into operations. It’s quite refreshing to not have to deal with the branding bludgeon that is digital twin. FM is a web and mobile content management system for 2D and 3D asset management, operations and maintenance. It combines floorplans, mapping and modelling based on location, aiding navigation. It offers a helpdesk ticketing system, work order generation, maintenance schedule and is a conduit for additional digitised documents, asset information, photos etc.

Again, the smart application uses GPS to position the user in floorplans and can be used in conjunction with QR codes for asset tagging or room tagging. The system comes with workflow tools to route tickets to the right department or person.

Handover is the Dalux product for packaging up and handing over design, construction and associated project information post build. Using templates, Handover can save a lot of time making sure the right information is used for FM downstream. It can output PDF reports and COBie files.

Tender is the secure app for distributing tenders on projects through Dalux and integrates to Dalux Field. Tender bids come with ready packaged up documents in a logical folder structure. The project owner remains in control and Dalux provides a full audit trail of any changes.

Conclusion

While US giants Procore and Autodesk Construction Cloud look to dominate the flow of data among construction and subcontractor firms, Dalux appears to be a European equivalent that is holding its own. However, the Dalgaard brothers have managed to keep the firm accessible to its customers and build a unique relationship.

As I understand, firms pay fees based on project size, as opposed to by number of users, meaning Dalux becomes the single source of truth for the construction data for all participants.

Dalux appears very support-centric, and it prioritises ongoing connection with developers and product champions in their customer base. It’s another reason why 1,800 people would visit Copenhagen to meet up with what felt more like a bespoke outsourced software developer, than a firm trying to meet next quarter’s targets.

Main image: In their morning address to attendees the Dalgaard brothers caught the zeitgeist perfectly

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