Twinmotion 2025.1.1, the latest release of the real time rendering software from Epic Games, supports Nvidia DLSS 4, a suite of neural rendering technologies that uses AI to boost 3D performance.

Epic Games shows that when DLSS 4 is enabled in Twinmotion it can render almost four times as many frames per second (FPS) than when DLSS is set to off.

DLSS 4 uses a technology called Multi Frame Generation, an evolution of Single Frame Generation, which was introduced in DLSS 3.

Single Frame Generation uses the AI Tensor cores on Nvidia GPUs to interpolate one synthetic frame between every two traditionally rendered frames, improving performance by reducing the number of frames that need to be rendered by the GPU.

Multi Frame Generation extends this approach by using AI to generate up to three frames between each pair of rendered frames, further increasing frame rates. The technology is only available on Nvidia’s new Blackwell-based RTX GPUs, which have been architected specifically to better support neural rendering.

Multi Frame Generation can be used alongside Super Resolution, where AI upscales a lower-resolution frame to a higher resolution, and Ray Reconstruction, where AI is used to generate additional pixel data in ray-traced scenes. According to Nvidia, when all DLSS technologies are combined, 15 out of every 16 pixels in a frame can be generated by AI. This greatly reduces the computational demands of traditional rendering and significantly boosts overall performance.

Twinmotion 2025.1.1 includes several other features.

3D Grass material allows users to drag and drop five types of grass material onto any surface. The Configurations feature, first introduced in Twinmotion 2025.1 to allow users to build interactive 3D presentations that showcase different variations of a project, has also been enhanced. Users can now export configurators to Twinmotion Cloud, for easy sharing, and use a mesh as a trigger — for example clicking on a door handle to open a door.

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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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With its stated aim of developing a next generation BIM tool to rival Revit, Motif’s initial offering was bound to be a small subset of what will be the finished product. In AEC Magazine, we have explained this many times before, but it’s worth saying again – the development of a Revit competitor is a marathon and all the firms that are out of stealth and involved in this endeavour (Qonic, Snaptrude, Arcol and Motif), will be offering products with limited capabilities before we get to detailed authoring of models.

Motif V1 is a cloud-based tool which aims to address a range of pain points in architectural engineering and construction workflows, particularly in the design presentation and review phases. From what we have seen of this initial offering, it’s clear that Motif has identified several features which you would typically find across a number of established applications – Miro, Revizto, Bluebeam, Speckle, Omniverse and many CDEs (Common Data Environments). This means that there’s no obvious single application that Motif really replaces, as it has a broad remit. Talking to CEO Amar Hanspal (read our interview), the closest application the company is looking to as a natural replacement for is Miro, which became popular during Covid for collaborative working. As it’s browser-based it works on desktop, laptop or tablet.

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Ideation assembly

The initial focus of the release is to enhance design review workflows by offering a more connected and 3D-enabled alternative to Miro. Users can collate 2D drawings, PDFs, SVGs and 3D models from a variety of different sources, to bring them into the Motif space for the creation of presentations, markup and collaboration.

The primary sweet spot is for collating project images and drawings into Concept presentations, using an ‘infinite canvas’ which can be shared with team members or clients in real time. Models can be imported from multiple sources and views snapshot, drawings from Revit added, material swatches for mood boards, images of analysis results, pretty much anything. These can be arranged collaboratively and simultaneously by multiple users and the software neatly assists in grid layout with some auto assistance. There’s also the ability to add comments for team members to see and react to.

Motif recognises that a data centric approach is essential in next generation tools. With this aim in mind, Motif borrows some ideas from Speckle, offering plugins for a variety of commonly-used design tools, such as Rhino and Revit. These plugins offer granular, bi-directional links to the cloud-based, collaborative Motif environment. One of the special capabilities is the live broadcasting of objects from Revit as they are placed, with Motif displaying the streamed model.

It’s possible to run Revit side by side with Motif, with Motif automatically synchronising views. As geometry is added to Revit it appears almost instantly in the Motif view. This is food for thought, as it makes live Revit design information available to collaborative teams. While this is Speckle-like there’s no need to set up a server or have high technical knowledge.

Motif facilitates granular sharing of information through “frames,” allowing users to select and share specific subsets of data with different stakeholders. The software translates data from native object models (e.g. Revit) into a ‘neutral internal object model’ (mesh and properties) which allows it to connect with different systems.
Buildings can be manipulated in 3D and there’s smart work plane generation. This might not be super useful right now, but we can imagine how it will play out once the BIM modelling tools get added in. For now, images can be applied to surfaces and freehand 3D markup and surface-based detection give the software an uncanny intuition for selecting surface planes and geometry when the mouse is near.

It’s possible to make markups to these ingested objects in Motif, and somewhat amazingly these comments can also be seen back in the Revit session. For now, though, there’s no clash detection or model entity editing available in Motif – its initial use is design review. Motif stores all the history at an object level, allowing users to go back in time to previous states of a project and see who changed what.

The product’s interface is wonderfully uncomplicated with only nine tools. The display feels very architectural, presenting ‘model in white’ with some grey shadowing.

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The data model

The underlying data model is important. Motif uses a ‘linked information model’ based on the idea that in AEC all data is distributed data. Instead of trying to centralise all the project information in a single system, which is what Autodesk Docs / Autodesk Construction Cloud (ACC) does, Motif aims to link data where it resides and assumes that no single system will have all the necessary information for a building. So instead of ingesting and holding all the data to be one version of the truth, somewhat trapping users in a file format, or cloud system, Motif will pull in data for display and reference reasons. In the future we guess it will be mixed with its own design information.
Motif is intended to be ‘pretty open’ according to the team, with plans to expose the API and SDK to allow users and developers access to extract and add their own data and object types.

At the moment the teams are developing plugins to connect Motif with various commonly-used BIM and CAD applications, including Grasshopper, Dynamo, SketchUp and AutoCAD, in addition to Rhino and Revit which are already supported.

Business model

At the early stage of most startups, having a sales force and actively selling an early version of an application is usually a low priority. Instead, many startups just seek early adopters for trial and feedback. Motif, while being in development for almost two years already has a small sales team and is actively selling the software for $25 a month per user. Hanspal says this is to ensure good discipline in software development, to provide scalability, performance, and responsiveness to customer feedback. The initial adoption is expected to come from companies looking to replace parts of their Miro workflow.

Conclusion

Motif fully intends to take on Autodesk Revit in the long term. CEO Hanspal realises this is a multi-year marathon, so while the team develops a modelling capability, it is utilising elements of its current technology to provide collaborative cloud-based solutions for a variety of pain points which they have identified as being under-serviced.

For now, the company aims to develop a cloud-based 3D interface for project information which will not necessarily replace existing BIM or drawing systems but will act as an aggregator and collaboration platform for those using a wide array of commonly used authoring tools. The software comes to market with an interesting array of capabilities, which may seem basic but provides some insight into what’s coming next – the bi-directional streaming between authoring tool and Motif, the deep understanding of Revit data, models and drawings, Revit synchronisation, connectivity to Rhino and smart interaction with model data all impress.

There may be some frustration with obvious capabilities that are currently omitted, such as simple clash detection between imported model geometry but we are sure this is coming as development progresses.

What Motif does, it does well. It’s hard to pigeonhole the functionality delivered when compared to any other specific genre of application currently on the market. Many will find it’s well worth having for the creative storyboarding alone, others may find collaborative design review the key capability. Those that can’t afford Omniverse might love the ability to have an application that can display all the coordinated geometry from multiple applications in the cloud for project teams to see and understand.

t’s important to remember that this is a work in progress and as the software develops its capabilities, it will expand into modelling and creating drawings. Its tight integration with Revit will be useful and reassuring
to those who want to mix and match BIM applications as the industry inevitably transitions to BIM 2.0.

Meanwhile, the Motif team continues to grow, adding in serious industry firepower. After hiring Jens Majdal Kaarsholm, the former director of design technology at BIG last year, the company has added Greg Demchak, who formerly ran the Digital Innovation Lab at Bentley Systems, as well as Tatjana Dzambazova formerly of IDEO. Demchak was an early recruit at Revit before Autodesk acquired it and Dzambazova was a long time Autodesk executive, deeply involved in strategy and development of AEC, reality capture and AI. It seems the old gang is getting back together.

Interview with Amar Hanspal, CEO, Motif

Martyn Day: For this first product, what was the rational in bringing out this subset of features. They seem quite disparate?

Amar Hanspal: What we are trying to do, over multiple years, is build out a system that you would call BIM, to provide everything you need to describe a building and create all the documents that are necessary to describe the building. There are four key elements, plus 1: modelling, documentation, data and collaboration. And then the plus one is scripting.

The data part is all about how it’s managed, stored, linked, represented and displayed for a customer, which is the user interaction model, around all of this. Scripting is just automation across all of these four things. And we have always thought about BIM that way.

We know people will react to the initial product because they see the user interface and think we are doing markup and sketching. But behind the scenes, these are just the two things that got ‘productised’ first, data handling and collaboration, while we build towards the other capabilities.

Our philosophy around data is, no matter how we store it, fundamentally, no system is going to have all of the data necessary for a building. So instead of trying, like ACC tries to centralise the information – and while you will always have some data in your system, I think the model we’re trying to bring to bear is a ‘link information model’, like the idea that you’re watching us bring with the plugins and the round tripping of the comments. We’re going to assume that data is going to stay where it is, and like the internet, we have to figure out a linking model, sharing model, to bring it together.

You can look at the app where it currently is, which features a couple of core concepts that we’re trying to bring to market – this distributed data idea, and then the second one is the user model on top of it, enabling sharing.

Martyn Day: You have been talking with leading AEC firms for two years. How will you go from this initial functionality to full BIM?

Amar Hanspal: We can’t wait ten years, like Onshape to Fusion to get all the capabilities in there. So what’s the sequencing of this? From sitting down and talking to customers, the design review process that they were implementing, we product we ran across the most was Miro. For design review many are using a Miro board. They would express frustration that it was just a painful, static, flat process. That’s where our ‘light bulbs’ went off. Miro is just collages and a bunch of information. Even when we become a full BIM editor, we’re still going to have to coexist with Tekla,  Rhino, Tekla, some MEP application. We actually have to get good at being part of this ecosystem and not demanding, demanding to be the source of truth for everything.

It gets us to the goal that we’re looking for, and we’re solving a user problem. So that’s how we came up with what we were going to do first, a Miro workflow mirror, and some companies are doing design interview using Adobe InDesign. Over time, we can become more capable of replacing some of the things that Bluebeam and Revizt

Martyn Day: With the initial release you have started selling the product, many start-ups put off developing sales to get early adoption?

Amar Hanspal: It’s good discipline. It’s like, eating your vegetables. When you ask people for money, you have to prove value. It’s good discipline for us to deliver something that’s useful to customers, and see them actually go through the process of making decision to spend money on it because they see how much it’s going to help or save them. That’s really obviously Martin, why we’re doing it. Just good discipline. Fundamentally, we want to make sure that we’re professional people developing software in a professional way, it forces us to be good about handing things like scalability, performance.

Read our extended interview with Motif CEO, Amar Hanspal

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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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Project Tandem, as it used be known, was initiated in February 2020, previewed at Autodesk University 2020, and released for public beta in 2021. Four years on, there are still significant layers of technology being added to the product, now focussing on higher levels of functionality beyond dashboards and connecting to IoT sensors, adding systems knowledge, support for timeline events and upgrades to fundamentals such as visualisation quality.

Tandem development seems to have followed a unique path, maintaining its incubator-like status, with Autodesk placing a significant bet on the future size of an embryonic market.

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For those following the development of Tandem the one thing that comes across crystal clear, is that creating a digital twin of even a single building — model generation, tagging and sorting assets, assigning subsystems, connecting to IoT, and building dashboards — is a huge task that requires ongoing maintenance of that data.

It’s not really ‘just an output of BIM’ which many might feel is a natural follow on. It has the capability to go way beyond the scope of what is normally called Facilities Management (FM), which has mainly been carried out with 2D drawings.

The quantitative benefit of building a digital twin requires dedication, investment and an adoption of twins as a core business strategy. For large facilities, like airports, universities, hospitals – anything with significant operating expenses – this should be a ‘no brainer’ but as with any investment the owner/operator has to pay upfront to build the twin, to realise the benefits in the long tail, measured in years and decades. This, to me, makes the digital twins market not a volume product play.

Tandem evolution

My first observation is that the visual quality of Tandem has really gone up a notch, or three. Tandem is partially developed using Autodesk’s Forge components (now called Autodesk Platform Services). The model viewer front end came from the Forge viewer, which to be honest was blocky and a bit crappy-looking, in a 1990s computer graphics kind of way. The updated display brings up the rendering quality and everything looks sharper. The models look great and the colour feedback when displaying in-model data is fantastic. It’s amazing that this makes such a difference, but it brings the graphics in to the 21st century. Tandem looks good.

As Tandem has added more layers of functionality the interface tool palettes have grown. The interface is still being refined, and Autodesk is now adopting the approach of offering different UIs to cater to different user personas, such as operators who might be more familiar with 2D floor plans than 3D.

Other features that have been added include the ability to use labels or floor plans to isolate them in the display, auto views to simplify navigation, asset property cards (which can appear in view, as opposed to bringing up the large party panel) and thresholds, which can be set to fire off alerts when unexpected behaviour is identified. Users can now create groups of assets and allocate them to concepts such as ‘by room’. Spaces can now also be drawn directly in Tandem.

Speed is also improved. As Tandem is database centric, not file based, it enables dynamic loading of geometry and data, leading to fast performance even with complex models. It also facilitates the ability to retain all historical data and easily integrate new data sources as the product grows. This is the way all design-related software will run. Tandem benefits from being conceived in this modern cloud era.

That said, development of Tandem has moved beyond simply collecting, filtering, tagging and visualising data to providing actionable insights and recommendations. From talking with Bob Bray, vice president and general manager of Autodesk Tandem and Tim Kelly, head of Tandem product strategy, the next big step for Tandem is to analyse the rich data collected to identify issues and suggest optimisations. These proactive insights would include potential cost savings and carbon footprint reduction through intelligent HVAC management based on actual occupancy data.

Systems tracing

Having dumb geometry in dumb spaces was pretty much the full extent of traditional CAFM. Digital twins can and should be way smarter. The systems tracing capability in Tandem simplifies the understanding of all the complex building systems and their spatial relationships, aiding operations, maintenance, and troubleshooting. By clicking on building system elements, you can see the connections between different elements within a building’s systems and see how networks of branches and zones relate to the physical spaces they serve and identify where critical components are located within the space. This means if something goes wrong, should that be discovered via IoT or reported by an occupant, systems tracing allows the issue to be pinpointed down to a specific level and room. Users can select a component like an air supply and then trace its connection down though subsystems to the spaces it serves.

Tandem is a cloud-based conduit, pooling information from multiple sources which is then refined by each user to give them insight into layers of spatial and telemetric data

Building in this connection between components to make a ‘system’, used to be a pretty manual process. Now, Tandem can automatically map the relationships between spaces and systems and use them for analysis to identify the root cause of problems. Timelines Data is valuable and BMS (Building Management Systems) and IoT sensors generate the building equivalent of an ‘ECG’ every couple of seconds. The historical, as well as the live data is incredibly valuable. Timelines in Tandem display this historic sensor data in a visual context. Kelly demonstrated an animated heatmap overlaid on the building model showing how temperature values fluctuate across a facility. It’s now possible to navigate back and forth through a defined period, either stepping through specific points or via animation, seeing changes to assets and spaces.

While the current implementation focuses on visualising historic data, Kelly mentioned the future possibility of the timeline being used to load or hide geometry based on changes over time, reflecting renovations or other physical alterations to the building.

Bray added that Tandem never deletes anything, implying that the historical data required for the timeline functionality is automatically retained within the system. This allows users to access and analyse past performance and conditions within the building at any point in the future, should that become a need.

Asset monitoring

Asset monitoring dashboards in Tandem are designed to provide users with a centralised view for monitoring the performance and status of their key assets. This feature, which is now in beta, aims to help operators identify issues and prioritise their actions. They will be customisable, and users can create dashboards to monitor the specific assets they care about This allows for a tailored overview of the most critical equipment and systems within their facility.

The dashboards will likely allow users to establish KPIs and tolerance thresholds for their assets. By setting these parameters, the system can accurately measure asset performance and identify when an asset is operating outside of expected or acceptable ranges with visual feedback of assets out of optimal performance.

Assets that are consistently operating out of tolerance or experiencing recurring issues can be grouped to aid focus e.g. by level, room, manufacturer. With this in mind, Tandem also has a ‘trend analysis’ capability, allowing users to identify potential future problems based on current performance patterns. The goal of these asset monitoring dashboards is to help drive preventative maintenance and planning for equipment replacement.

Tandem Connect

Digital Twin creation and connectivity to live information means there is a big integration story to tell and it’s different on nearly every implementation. Tandem is a cloud-based conduit, pooling information from multiple sources which is then refined by each user to give them insight into layers of spatial and telemetric data. To do that, Autodesk needed to have integration tools to tap into, or export out to, the established systems, should that be CAFM, IoT, BMS, BIM, CAD, databases etc.

Tandem Connect is designed to simplify that process and comes with prepacked integration solutions for a broad range of commonly used BMS. IoT and asset management tools. This is not to be confused with other developments such as Tandem APIs or SDKs.

The application was acquired and so has a different style of UI to other Autodesk products. Using a graphical front end, integrations can be initially plug and play, such as connecting to Microsoft Azure, through a graph interface. The core idea behind this is to ‘democratise the development of visual twins’ and not require a software engineer to get involved. However more esoteric connections may require some element of coding. Bray admitted there was significant ‘opportunity for consultancy’ that arises from the whole connectivity piece of the pie and that a few large system integrators were already talking with Autodesk about that opportunity.

Bray explained that Tandem Connect enables not only data inflow and outflow but also ‘workflow automation and data manipulation’. He gave an example where HVAC settings could be read into Tandem Connect, and a comfort index could be written, which was demonstrated at Autodesk University 2024.

Product roadmap

Autodesk keeps a product roadmap which has been pretty accurate to show the development of travel, given the regular video updates.

Two of the more interesting capabilities in development are portfolio optimisation and the development of more SDK options, plus the possibility of future integration of applications. Portfolio optimisation will allow users to view data of multiple facilities in one central location and should provide analytics to predict future events with suggested actions for streamlining operations.

Beyond the current Rest API (Now), Autodesk is developing a full JavaScript Tandem SDK to build custom applications that leverage Tandem’s logic and visual interactivity. In the long-term, Autodesk says it will possibly enable extensions for developers to include functionality within the Tandem application itself.

Conclusion

Tandem development continues relentlessly. The capabilities that are being added now are starting to get into the high value category. While refinements are always being added to the creation and filtering, once the data is in and tagged and intelligently put into systems, it’s then about deep integration, alerts for out of nominal operation at a granular level, historical analysis of systems, spaces and rooms, all with easy visual feedback and the potential for yet more data analysis and intelligence.

Bray uses a digital twin maturity model to outline the key stages of development needed to realise the full potential of digital twin technology. It starts with building a Descriptive Twin (as-built replica), then Informative Twin (granular operational data), then Predictive Twin (enabling predictive analytics), Comprehensive Twin (what-if simulation) and Autonomous Twin (self-tuning facilities).

At the moment, Tandem is crossing from Informative to Predictive, but the stated intent for higher level functionality is there. However the warning is, your digital twin is only ever as good as the quality of the data you have input.

Some of the early users of Tandem are now being highlighted by the company. In a recent webinar, Brendan Dillon, director of digital facilities & infrastructure, Denver International Airport gave a deep dive into how they integrated Maximo with Tandem to monitor facility operations.

Tandem is an Autodesk outlier. It’s not a volume product and it’s not something that Autodesk’s channel can easily sell. It’s an investment in a product development that is quite unusual at the company. It doesn’t necessarily map to the way Autodesk currently operates as, from my perspective, it’s really a consultancy sale, to a relatively small number of asset owners – unlike Bentley Systems, whose digital twin offerings often operate at national scale across sectors like road and rail. The good news is that Autodesk has a lot of customers, and they will be self-selecting potential Tandem customers, knowing they need to implement a digital twin strategy and probably have a good understanding of the arduous journey that may be. The Tandem team is trying to make that as easy as possible and clearly developing it out in the open brings a level of interaction with customers that in these days is to be commended.

Meanwhile, with its acquisition of niche products like Innovyze for hydraulic modelling, there are some indications that Autodesk is perhaps looking to cater to more involved engagements with big facility owners, and I see Tandem as falling into that category at the moment, while the broader twins market has still yet to be clearly identified.

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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.

The post Regarding digital twins appeared first on AEC Magazine.

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For almost twenty years the AEC software world was centred around Autodesk Revit and its definition and workflow of BIM. The concept was to ideate, model detail designs and create all the necessary drawings in one monolithic platform.

But software typically has a lifespan, where it needs to be rewritten or rearchitected (for OS changes, new hardware, and to clean-up years of bloat).

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Following open letters from customers concerned at the lack of Revit development Autodesk explained that it was not going to rewrite Revit for the desktop, but instead would develop a next generation AEC design environment on the cloud, branded Forma (N.B. Carl Christensen, the Autodesk VP in charge of delivering Forma, will be presenting at NXT BLD on June 11).

This gap between Revit and what will come next has presented an opportunity for new software developers to rethink BIM and its underlying technologies, to bring the AEC design software into the 21st Century. Investors have become equally excited and NXT BLD and NXT DEV will provide a unique forum for multiple startups—Snaptrude, Motif, Qonic and Arcol—to present new commercial BIM 2.0 products, with more firms in stealth, probably in the audience!

While the velocity of the startups is impressive, we need to temper expectations by pointing out that competing against established desktop BIM applications, which are 20+ years old, will take years (and millions of dollars). Over the coming years, expect to see these tools become more feature comparative.

While BIM 2.0 shifts the focus away from producing drawings, there’s no escaping their continued importance to the AEC industry. That’s why there’s also a big focus on autodrawings, as this AI-powered technology promises to massively reduce the time spent doing the mundane boring work. Autodrawings could also mean fewer licences of BIM software are required. Both Snaptrude and Qonic have developments here. However, it’s quite possible that autodrawings and AI will become cloud services that don’t need to be in an all encompassing BIM platform.

At NXT BLD / DEV you can meet and engage with all these firms, plus many more individuals innovating in the AEC space, such as Antonio González Viegas of ThatOpenCompany and Dalai Felinto of Blender bringing the benefits of impressive Open Source tools to our industry. We hope that you will join us.

NXT BLD 2025
London
11 June 2025
www.nxtbld.com

NXT DEV 2025
London
12 June 2025
www.nxtdev.build

Arcol

Based in New York, Arcol is headed up by Irishman, Paul O’Carroll, who brings a games development background to BIM and 3D. One of the earliest to profile its approach as ‘Figma for BIM’, the company has attracted investors such as chief executives of both Procore and Figma.

Arcol has focussed heavily on concept design for its initial offering, enabling live in-context modelling with building metrics and data extraction and collaboration built-in. The software supports complex geometry, an easy to learn UI, board creation for presentations (which can be shared by just sending a link), live plans and sheets. It integrates with Revit, SketchUp and Excel. Reports are highly visual and Arcol see it as a replacement for PDF as well. The solution is aimed at architects, developers, general contractors and owners. Arcol will be officially shipping by the time of NXT BLD.

Motif

Motif is headed up by former joint CEO of Autodesk, Amar Hanspal, who has assembled the old gang to finish off a task he started in 2016 – the rewriting of Revit as a cloud application.

Motif is also pitched as Figma for BIM and is backed by Alphabet (Google) with a sizeable war chest. In stealth for the last two years, the company has been working with signature architects to learn what a BIM 2.0 application should be able to do – the idea being that by catering to the most demanding customers, the software should benefit everyone.

The company has just launched its first version but recognises the journey will take many years. The feature set of version 1 lends itself to design review and client presentations, taking aim at Miro, but with some Speckle and Omniverse like capabilities.

Qonic 

The origins of Ghent-based Qonic go back to TriForma, a BIM system which co-founder Erik de Keyser created and licensed to Bentley Systems. de Keyser then created BricsCAD and Bricsys – a DWG and formative BIM tool, which was later sold to Hexagon.

Many of the Bricsys team then started up Qonic, a cloud-based BIM 2.0 competitor which initially (and uniquely) focuses on the model and data interface between architecture and construction. Qonic can load huge Revit models and lets users fly through them with butter smooth refresh rates on the desktop or mobile. The program also has powerful solid modelling core for geometry edits, as well as supporting IFC component labelling. The initial release is exceptionally easy to use to see, manipulate and filter BIM data, as a CDE on steroids. The team is working on architectural tools, smart drawings and a range of features to expand capabilities.

Snaptrude

Snaptrude has the accolade of being the first BIM 2.0 startup that AEC Magazine discovered. CEO Altaf Ganihar was first to demonstrate cloud-based collaborative working on Revit models and has gone on to raise $21m in VC funding.

The New York-based company seeks to be a one stop shop for conceptual, detailed design and drawing production, while linking to all the common tools – Revit, SketchUp, AutoCAD, Rhino, as well as Nemetschek’s Archicad. Snaptrude currently offers the widest range of BIM 2.0 features from concept to AI renderings and drawings and looks as if it will probably be first with feature parity to Revit for Architecture, with plans to also support MEP and structural. With the biggest development team in the BIM 2.0 space the company is moving at pace to deliver on its aims. The company is soon to announce a range of major new features.

Main image caption: Antonio González Viegas, CEO of That Open Company, the creator of free and open technology that helps AECO software firms and practitioners create their own AECO software, will be speaking at NXT DEV again this year.

The post Future BIM voices at NXT BLD / DEV appeared first on AEC Magazine.

We are only halfway into the 2020s, yet this decade has already been marked by some unparalleled tech developments in AEC town. Riding on the paradigm-shifting AI wave with an explosion in server infrastructure, more and more workflow innovations in our industry tend to be cloud-native. Furthermore, the shifts and increasing flexibilities in our work cultures are fuelling the need for digital tools and solutions that run entirely in the web browser.

I can hear some of you arguing that cloud-based software is just the same features as traditional desktop software, moved into the browser… but it is well beyond that. It is about improving collaboration and security.

If you have ever used solutions like Google Docs, Salesforce, Slack, or GitHub, you know what I am talking about.

And it is fair to say that the AEC world is late in adopting this approach. Compare AEC with Manufacturing, for example, which already has well-established cloud-based solutions such as PTC Onshape, the 3DEXPERIENCE platform, and Fusion 360, to name just a few…

At AEC+Tech, we have been fortunate to test, review, and host a number of innovative design technology products, spanning early-stage design, massing and form evaluation, performative assessments, collaborative decision-making, or project and facility management, you name it. These tools are extremely competent in augmenting your design process, and solving design-stage or management problems effectively.

But something that these cloud-native workflows lack is the downstream support for drawing creation: as projects progress and become increasingly definitive, the generation of drawings regularly over the many project phases becomes crucial. True, you may harness the cloud capabilities of an AECTech product, work on the fly, and collaborate with your colleagues as you make your decisions. But when you move from design to drawing, you still have to rely on conventional (and often expensive) BIM and CAD solutions to go forward. And the biggest downside of that transition? The workflow has to be detached from the cloud and run on your machine instead, owing to the sheer size of these BIM/CAD software installations.

What these cloud-native design and management software applications need to complement them — and thus complete their workflows — is a robust option for drawings generation. Ideally, this solution would not only run on the cloud, but would also possess features that substantially accelerate and streamline the drawings creation process. And we at AEC+Tech have identified this exact cloud-native drawing creation solution, for developers to reinforce and complete their workflows: Graebert’s ARES Kudo.

ARES Kudo is a DWG-based Online CAD solution available both to end-users and as a CAD development platform. The ARES Kudo platform is used by many leading online solutions, including PTC Onshape, xDraftSight, and Trimble Connect.

What is ARES Kudo?

ARES Kudo is an online, DWG-based 2D CAD solution that runs in your web browser. It is both a robust stand-alone product and a part of the ARES Trinity of CAD software, along with ARES Commander and ARES Touch.

Thanks to a thoughtfully designed user interface that’s very familiar to traditional CAD users, shifting from a conventional CAD product to Kudo has a nearly flat learning curve. Plus, Kudo lets multiple users collaborate on the same file simultaneously — something that conventional tools hardly offer.

What really sets Kudo apart, however, is a unique amalgamation of two powerful elements: a full CAD-on-the-web solution, and Drawings Automation technology. Let’s look at both of these in more detail.

A Complete CAD-on-the-Web Solution

ARES Kudo includes a full DWG editing package that operates on any web browser. It gives you immediate access to all the tools and functions needed to create, edit, and customize drawings. As expected, you can also generate PDF or other outputs from your DWG files.

Plus, Kudo comes with a host of features to work collaboratively as a team in an instant — a signature advantage of cloud-native software. Users can add annotations, comments, and markups while working together on any DWG file. Conflicting changes are never a problem, since access to the editing capability is transferred via permissions from one user to the next.

Moreover, Kudo comes with ARES Online Drawings Automation features, enabling you to compare two DWG drawings to track changes, for example, or extract data from DWG files to CSV for schedules and quantities. Other Automation capabilities include the batch conversion of PDF or DGN files to DWG, which turns these files into drawings that can be edited in ARES Kudo. And Graebert has also announced that it will soon be possible to automatically batch-print Sheet Sets to PDF.

These functionalities make the software much more capable and robust than a conventional CAD solution, and would be a welcome complement to the early-stage design/BIM pipeline of major, emerging AECTech products out in the market.

What also makes ARES Online Drawings Automation unique in the market is the ability to schedule recurring jobs. As an end user, you could use ARES Kudo to schedule a task such as reading all your DWG files each night and converting them to PDF.

And as a developer, you could simply automate many tasks by default, anticipating the needs of your users. For instance, if they upload PDF, DGN, or Revit files, you might expect that sooner or later they will need to markup or edit them — so you could proactively convert these file types to DWG drawings upon import.

Drawings Automation from BIM Data

Perhaps the most exciting feature for developers to adopt into their pipeline is the automated generation of DWG drawings from Revit or IFC projects. Since most early-stage design tools put out a BIM (RVT or IFC) file, instant creation of drawings from this data will be a game-changer for AECTech developers.

ARES Kudo stands out due to its capabilities to read and extract building information and properties along with other data associated with BIM objects. This is possible because it imports entire BIM projects rather than just their geometry. Having immediate access to comprehensive building data — encompassing dimensions, thermal properties, schedules, material, colour, texture, costs, etc. — enhances the process of creating drawings, and enables Drawings Automation.

Upon importing a BIM project, Kudo reads the properties of the objects, and based on the nature of the project, automatically creates all the drawings you need (think plans, sections, and elevations). It then adds smart dimension chains, annotations, and labels on your drawings, by reading the attributes of the associated BIM objects. Simply speaking, this significantly reduces the manual input for drawing creation when compared with programs like Revit.

Integrating ARES Online Drawings Automation into your AECTech product will unlock a more complete pipeline for users to start from early-stage design and go all the way to enriched drawings — all on the cloud.

How Does the New ARES Online Drawings Automation Work in ARES Kudo?

As most cloud-native solutions do, Kudo executes the automated tasks on a distant server. Consequently, you don’t have to wait until the job is done; you can work on something else with your computer while the automation progresses.

Plus, you can batch-process multiple files and schedule recurring tasks with ARES Kudo. For example, you could set it to launch your desired  Drawings Automation routines every night, and track the progress of your BIM project as it evolves.

Developers using ARES Kudo as a development platform can create their own custom settings for the drawings the automation produces. You could achieve, for example, four different styles of drawings from the same geometry. When comparing the four drawings below, you can see how the representation of the walls, the dimensions, and the labels are different in each one, thanks to the high degree of customization possible.

Furthermore, developers have access to the ARES Online Drawings Automation technology, which can be used independently or in conjunction with the online CAD features provided by the ARES Kudo Development Platform for DWG editing. This integration allows for the creation of custom solutions tailored to specific needs for online DWG editing.

For AECTech companies creating software/solutions for design, BIM, project and facility management, cost estimation, and so on, the possibility to focus almost entirely on the 3D features and automate all the 2D afterwards is certainly innovative. As a process running automatically in the background and in real time, with the user designing in 3D with BIM or uploading a RVT/IFC file, Kudo solves a crucial problem — enabling developers to upscale and mainstream their technology to a broader audience.

How is the ARES Kudo Development Platform Benefiting Developers Already?

Join the Developer Workshop co-hosted by Graebert and Martyn Day from AEC Magazine

• The ARES Kudo Online CAD Development platform
• The new ARES Online Drawings Automation technology for developers
• Examples of integrations with industry-leading solutions

Panel discussion with industry experts:
How Cloud and Drawings Automation Are Transforming the CAD Industry

Drawings Automation in Link with Snaptrude

While the Drawings Automation technology in ARES Kudo is being officially introduced at the Graebert neXt event in April, it was previously introduced in the desktop version, ARES Commander. It has also been used to complement the BIM-based workflow of the AECTech product called Snaptrude: a cloud-native, BIM-based collaborative design platform. Read this previous article to learn more and see this integration in video form: “Drawings Automation for BIM Projects (AEC Magazine, March 27, 2024).”

This partnership stands as testimony to how well ARES Kudo integrates with other developers’ tools, and to the vast possibilities it opens up for collaboration to upscale and mainstream the use of new AECTech solutions.

10 Years of Collaboration with Onshape

Onshape is a fully cloud-based 3D CAD platform that revolutionizes product design by enabling real-time collaboration and eliminating the need for traditional file-based workflows. A key component of Onshape’s functionality is its Drawings module, which is based on Graebert’s ARES Kudo technology.

This partnership integrates Graebert’s advanced online CAD technologies into Onshape, enabling users to create detailed 2D drawings that are automatically synchronized with their 3D models.

15 Years of Collaboration with Dassault Systèmes for DraftSight

The partnership between Graebert and the industry-leading 3D giant behind SOLIDWORKS and CATIA, Dassault Systèmes, around DraftSight began roughly 15 years ago. It soon blossomed into a hugely successful story, yielding a 2D DWG-based CAD desktop application that quickly amassed a massive user base reaching more than 10 million users worldwide.

Over time, this collaboration expanded beyond the core desktop product into integrations with other popular Dassault Systèmes products like GEOVIA, ENOVIA, HomeByMe, 3D ContentCentral, SOLIDWORKS PDM, and now, importantly, the 3DEXPERIENCE online collaboration platform.

This year, the desktop-based higher-end version, DraftSight Premium, is also introducing Graebert’s BIM drawings automation features. Most recently, the partnership produced xDraftSight — a fully cloud-based version of DraftSight built on Graebert’s ARES Kudo engine. Accessible through a web browser as part of the 3DEXPERIENCE platform, xDraftSight enables users to edit DWG drawings online with the same capabilities as the desktop, while enjoying seamless integration with Dassault’s cloud data and collaboration tools.

To sum up,

ARES Kudo and the online CAD development platform associated with it offer an array of new possibilities for AECTech product developers. Needless to say, Kudo may be the most robust cloud-native drawing tool out there. Its bespoke BIM automation features, coupled with its ability to complement other products’ workflows and project pipelines, positions it as a strong, indispensable player in this area.

With an increasing number of developers and companies exploring partnerships to nest Kudo’s capabilities in their pipelines, it is set to disrupt conventional workflows — for the better. With all the new collaboration and opportunities unfolding as we speak, these are some very exciting times for the AECTech space!

The post ARES Kudo: DWG Drawings Automation for Developers appeared first on AEC Magazine.