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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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While AI is in full hype cycle and not a day passes without some grandiose AI claim, there are some press releases that raise the wizzened eyebrows at AEC Magazine HQ. North Carolina-based start-up, Higharc, has demonstrated a new AI capability which can automatically convert 2D hand sketches to 3D BIM models within its dedicated housing design system. This type of capability is something that several generic BIM developers are currently exploring in R&D.

Higharc AI, currently in beta, uses visual intelligence to auto-detect room boundaries and wall types by analysing architectural features sketched in plan. In a matter of minutes, the software then creates a correlated model comprising all the essential 3D elements that were identified in the drawing – doors, windows, and fixtures.

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Everything is fully integrated with Higharc’s existing auto-drafting, estimating, and sales tools, so that construction documents, take-offs, and marketing collateral can be automatically generated once the design work is complete.

In one of the demonstrations we have seen, a 2D sketch of a second floor is imported, analysed and then automatically generates all the sketched rooms and doors, with interior and exterior walls and windows. The AI generated layout even means the roof design adapts accordingly. Higharc AI is now available via a beta program to select customers.

Marc Minor, CEO and co-founder of Higharc explains the driving force behind Higharc AI. “Every year, designers across the US waste weeks or months in decades-old CAD software just to get to a usable 3D model for a home,” he says.

“Higharc AI changes that. For the first time, generative AI has been successfully applied to BIM, eliminating the gap between hand sketches and web-based 3D models. We’re working to radically accelerate the home design process so that better homes can be built more affordably.”

AI demo

In the short video provided by Higharc, we can see a hand drawn sketch imported into the Autolayout tool. The sketch is a plan view of a second floor, with bedrooms, bathrooms and stairs with walls, doors and windows indicated. There are some rough area dimensions and handwritten notes, denoting room allocation type. The image is then analysed. The result is an opaque overlay, with each room (space) tagged appropriately, and a confirmation of how many rooms it found. There are settings for rectangle tolerance, minimum room areas. The next phase is to generate the rooms from this space plan.

We now switch to Higharc’s real-time rendered, modelling and drawing environment, where each room is inserted on the second floor of an existing single floor residential BIM model, where walls, windows, doors and stairs are added, and materials are applied. This is simultaneously referencing an image of the sketch. The accurate BIM model has been created, combining traditional modelling with AI sketch-to-BIM generation.

What is Higharc?

Founded in 2018, Higharc develops a tailored cloud-based BIM platform, specifically designed to automate and integrate the US housing market, streamlining the whole process of design, sales, and constructing new homes.

Higharc is a service sold to home builders, that provides a tailored solution which integrates 3D parametric modelling, the auto creation of drawings, 3D visualisations, material quantities and costing estimates, related construction documents and planning permit application. AEC Magazine looked at the development back in 2022.

The company’s founders, some of which were ex-Autodesk employees, recognised that there needed to be new cloud-based BIM tools and felt the US housing market offered a greenfield opportunity, as most of the developers and construction firms in this space had completely avoided the BIM revolution, and were still tied to CAD and 2D processes. With this new concept Higharc offered construction firms easy to learn design tools, which even prospective house buyers could use to design their dream homes. As the Higharc software models every plank and timber frame, accurate quantities can be connected to ERP systems for immediate and detailed pricing for every modification to the design.

The company claims its technology enhances efficiency, accelerating a builder’s time to market by two to three times, reducing the timeline for designing and launching new plots by 75% (approximately 90 days). Higharc also claims that plan designs and updates are carried out 100 times faster than with traditional 2D CAD software.

To date, Higharc has raised $80 million and has attracted significant investment and support from firms such as Home Depot Ventures, Standard Investments, and former Autodesk CEO Carl Bass. The company has managed to gain traction in the US market and is being used to build over 40,000 homes annually, representing $19 billion in new home sales volume.

While Higharc’s first go to market was established house building firms, the company has used money raised to expand its reach to address those who want to design and build their own homes. The investment by Home Depot would also indicate that the system will integrate with the popular local building merchants, so selfbuilders can get access to more generic material supply information. The company also plans to extend the building types it can design, eventually adding retail and office to its residential origins.

In conversation

After the launch, AEC Magazine caught up with co-founder Michael Bergin and company CEO Marc Minor to dig a little deeper into the origin of the AI tool and how it’s being used. We discovered that this is probably the most useful AI introduction in any BIM solution we have seen to date, as it actually solves a real world problem – not just a nice to have of demoware.

The only reason we were able to do it, is because of what Higharc is in the first place. It’s a data-first BIM system, built for the web from the ground up

In previous conversations with Higharc, it became apparent that the company had become successful, almost too successful, as onboarding new clients to the system was a bottleneck. Obviously, every house builder has different styles and capabilities which have to be captured and encoded in Higharc but there was also the issue of digital skill sets. Typically, firms that were opting to use Higharc were not traditional BIM firms – they were housebuilders, more likely to use AutoCAD or a hand drawn sketch, than have much understanding of BIM or modelling concepts. It turns out that the AI sketch tool originated out of a need to include the non-digital, but highly experienced, house building workforce.

Mark Minor: The sketch we used to illustrate at launch is a real one, from one of our customers. We have a client, a very large builder in Texas who builds 4,000 houses per year just in Texas. They have a team of 45 or so designers and drafters, and they have a process that’s very traditional. They start on drawings boards, just sketching. They spend three months or so in conceptual design and eventually they’ll pass on their sketches to another guy who works on the computer, where he models in SketchUp, so they can do virtual prototype walk-throughs to really understand the building, the design choices, and then make changes to it.

The challenge here is that it takes a long time to go back and forth. We showed them this new AI sketch to model work we were doing, and they gave us one of their sketches for one of their homes that they’re working on. The results blew their mind. They said for them ‘this is huge’. They told us they can cut weeks or months from their conceptual stage and probably bring in more folks at the prototype walk-through stage. It’s a whole new way of interacting with design.

What makes this so special, and is the only reason we were able to do it, is because of what Higharc is in the first place. It’s a data-first BIM system, built for the web from the ground up. Because it’s data first, it means that we can not only generate a whole lot of synthetic data for training rapidly, but we really have a great target for a system like this – taking a sketch and trying to create something meaningful out of the sketch. It’s essentially trying to transform the sketch into our data model. And when you do that, you get all the other features and benefits of the Higharc system right on top of it.

Martyn Day: As the software processes the file, it seems to go through several stages. Is the first form finding?

Mark Minor: It’s not just form finding, actually, it’s mapping the rooms to particular data types. And those types carry with them all kinds of rules and settings.

Michael Bergin: At the conceptual / sketch design phase these are approximate dimensions. Once you’ve converted the rooms into Higharc, the model is extremely flexible. You can stretch all the rooms, you can scale them, and everything will replace itself and update automatically. We also have a grid resolution setting, so the sketch could even be a bubble diagram, or very rough lines, and you just set the grid resolution to be quite high, and you can still get a model out of that.

Higharc contains procedural logic, as to how windows are placed, how the foundation is placed, the relationships between the rooms. So the interaction that you see as the AI processes the sketch and makes the model, places the window, doors and the spaces between the rooms, that is all coming from rules that relate to the specifications for our builder.

Martyn Day: If doors collide, or designs do not comply with local codes, do you get alerted if you transgress some kind of design rule?

Michael Bergin: We have about 1,000 settings in Higharc that relate to the building that are to adjust for and align to issues of code compliance. When you get into automated rule checking, evaluating and digesting code rules and then applying that to the model, we have produced some exciting results in more of a research phase in that direction. There’s certainly lots of opportunities to express design logic and design rules, and we’ll continue to develop in that direction.

Mark Minor: One of the ways we use this, is we go to a home builder we want as a customer. In advance of having a sales chat, we’ll actually go to their website and screenshot one of their floor plans. We’ll pull it the AI tool and set it up as the house. We want to help folks understand that it’s not as painful and as hard as you might think. The whole BIM revolution happened in commercial, that’s kind of what’s happening in home building now. But 90% or more of all home builders use AutoCAD. We rarely come across Revit.

Martyn Day: I can see how you can bring non-digital housebuilders into the model creation side of things, where before everything would be handled by the computer expert. With this AI tool, does that mean suddenly everyone can contribute to the Higharc model?

Michael Bergin: Yes! That’s extremely important to us, bringing more of the business into the realm of the design, that’s really the core of our business. How do we bring the purchasing and the estimating user into the process of design? How do we take the operations user who’s scheduling all of the work to be done on the home into the design, because ultimately, they all have feedback. The sales people have feedback. The field team have feedback, but they’re all blocked out. They are always working through an intermediary, and perhaps through an email to a CAD operator. It goes into a backlog. We are cutting that distance between all the stakeholders in the design process and the artefact of the design has driven a lot of our development.

It’s exciting to see them engaging in the process, to see new opportunities opening up for them, which I think is broadly a great positive aspect of what’s happening with the AI revolution.

Martyn Day: You have focused on converting raster images, which is hard, as opposed to vector. But could you work with vector drawings?

Michael Bergin: While it would have been easier to use a vector representation to do the same AI conversion work, the reason that we did focus on raster was that vector would have been quite limiting. It would have blocked us out from using conceptual representations. If our customers are using a digital tool at all, they are building sketches in something like Figjam. In this early conceptual design stage, we have not seen the Rayon tools or really any of the new class of tools that the market is opening up for. Our market in US home builders tends to be the way that they’ve been doing things for some decades, and it works well for them, and we are fortunate that they have determined that Higharc is the right tool for their business.

Making it possible that the businesses process can change has required us to develop a lot of capabilities like integrating with the purchasing and estimation suite, integrating with the sales team, integrating with ERPs, really mirroring their business. Otherwise, I don’t think that we would have an excellent case for adoption of new tools in this industry.

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Precise reality capture has come a long way. We are in the process of moving from rare and expensive to cheap and ubiquitous. Laser scanning manufacturers are currently holding their price points and margins, but technology and mobility are closing in from the consumer end of the market. Matterport recently launched a low-cost laser scanner combined with photogrammetry, and Polycam, a developer of smartphone-based reality capture software for consumers, is looking to sell up to the professional market.

Polycam can be used to quickly document existing conditions (as-builts), measure spaces, and generate floor plans. The latest release looks to dig deeper into AEC workflows. The app is available for iOS and Android and makes use of the iPhone’s built in LiDAR and cameras to capture interiors and exteriors when using footage from a drone. The software also supports Gaussian Splats to achieve high-resolution 3D capture. While the product has proved incredibly popular, the firm is looking to move into new areas of AEC, such as interior design, structural, construction inspection and facilities management.

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The company

Polycam was founded four years ago by Chris Hinrich and Elliot Spellman. Their initial aim was to build software that could deliver the power of 3D capture to users of smartphones.

Before Polycam, the pair worked at a company which was developing a ‘3D Instagram’ that processed uploaded images on a server for photogrammetry. This was a bottleneck. The pair left the company and set up Polycam. The big innovation was the fact that you could process the 3D creation fast, on device.

With over half the Fortune 500 companies actively using Polycam and well over 100,000 paying users, the firm has been able to raise over $22 million in 2024 in investment, based on revenues of $6.5 million in 2023. One of the core areas that showed regular growth was in their AEC user-base. The latest release focuses on providing tools for the growing base of AEC customers.

New features

Polycam supports Apple’s AR toolkit, allowing for easier and more accurate model creation by recognising walls, doors, and windows. I have used Polycam on my iPhone and compared it to a Leica Disto and have found the accuracy to be within a few millimetres when scanning a room. This makes it suitable for schematic designs and perhaps material ordering (though precise cuts might still require manual measurements). The platform supports multifloor scanning, to build a model very similar to that of Matterport.

While an automated scan-to-BIM workflow is seen as the aim, Polycam offers a service where users can order professional-grade 3D files that are then converted into CAD (AutoCAD) and BIM (Revit) files – but with a human-in-the-loop, through a collaboration with Transform Engine. This provides a higher quality and more detailed BIM output than automatic processing currently offers. AutoCAD layouts start at $95 and Revit models $200. Furthermore, Polycam has plans to add IFC (Industry Foundation Classes) file export, which will make it easier for users to create their own models.

That said, Polycam does instantly generate customisable 2D floor plans from its scans. These floor plans can be tweaked within the app for business and enterprise tiers, allowing for adjustments to wall thickness, colours, and labels.

Complex geometry can fool the application. I found that accurately capturing ceilings with multiple levels and stairs, resulted in gaps in the models

There’s a new AI Property Report, which automatically generates PDFs and includes the floor plan along with information such as the number of bedrooms and bathrooms, floor area, total wall area, and a room-by-room breakdown with measurements. This could be used for insurance or costing and ordering materials. The AI automatically derives room classifications by detecting objects like beds (for bedrooms) and appliances (for kitchens).

The new Scene Editor allows multiple scans to be combined, including both interior captures and drone footage, into a single, unified 3D scene. This provides a holistic view of a property or project site, enabling users to navigate and analyse the entire space. Using layers, it’s possible to filter scenes and control the visibility of different parts of a capture.

The platform also has new collaboration and sync tools that allow users to add comments and start threaded conversations within a scanned space, facilitating review processes for architects and other stakeholders. The cross-platform nature of Polycam ensures that teams can access and share this data across various remote devices.

3D Generator

The latest version offers a quick way of making 3D components for a library, from real world objects like a chair, starting from an image or a prompt, describing the details of the object you would like to create. This isn’t just the geometry, but the materials used too. These 3D objects can be placed in the real-world scans, enabling users to visualise and design spaces with custom virtual objects.

Limitations

Because everything is on device and there is no option for cloud or serverbased processing, there comes a natural limit. On-device memory is also a constraint. Polycam recommends a horizontal size limit of around 279 sq metres for a single scan, to ensure a decent result. Beyond this, the app might require compromises to process quickly without running out of memory. While the new scene editor addresses combining multiple scans, individual scans still have practical size limits.

Complex geometry can fool the application. I found that accurately capturing ceilings with multiple levels and stairs, resulted in gaps in the models. While the technology has improved, complex or non-planar geometry in older buildings might still present some challenges.

While Polycam is accurate enough for schematic designs and potentially ordering bulk materials (the company claims within 2% compared to expensive LiDAR scanners), it might not be sufficient for tasks requiring very high precision, such as cutting kitchen cabinets, which may still necessitate manual measurements. Also while using the AR Toolkit object recognition the spatial reports is not totally foolproof and may require users to manually override classifications if they are incorrect.

Polycam seems to have approached the market more aimed at construction and its use in the American market. While this is predominantly 2D, the BIM side of the product has a lot yet to be delivered connecting the data on device to BIM software. Scan-to-BIM still requires the cost and eye of a human to properly check the conversion. This has to be compared to having a professional survey and the legal indemnity that it provides. Would I use Polycam on a house? Hell yes! Would I use it on a major airport refurbishment? Only as a quick rough.

Conclusion

Polycam is certainly on the right path with its concentration of development of instant 2D floor plan generation and measurements, as well as building 3D models for AEC users. AR Toolkit’s intelligence always seems like magic when scanning a room. However, the software and service does have limitations with obvious omissions and the need for closer integration with AEC workflows. Surely, we can’t be too far away from not requiring a human in the loop to create reliable results from scan to BIM?

Size matters. While the possibility of real-time streaming of large-scale scans is a compelling idea for future development, the current focus of Polycam appears to be on enhancing on-device processing and providing relevance to the AEC industry. The planned addition of features like IFC export and improved BIM workflows indicates a clear direction towards serving the professional needs of architects, engineers, and construction professionals.

Despite these limitations, the monthly use cost is $17 per user (Pro) and $34 per user (Business level). At those prices, it’s an application that many in the industry might well use regularly, when onsite vs the alternative. This is like having a budget Matterport scanner in your pocket.

The ongoing development and the specific features being introduced demonstrate a clear trajectory towards making Polycam a better fit for AEC professionals, especially surveyors and architects, particularly for initial site assessment, as-built documentation, schematic design, and collaboration.

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The magazine also includes a whopping 40-page Workstation Special Report dedicated to the very latest technology for Architecture, Engineering and Construction (AEC) workflows.

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

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While AI is in full hype cycle and not a day passes without some grandiose AI claim, there are some press releases that raise the wizened eyebrows at AEC Magazine HQ.

North Carolina-based start-up, Higharc, has demonstrated a new AI capability which can automatically convert 2D hand sketches to 3D BIM models within its dedicated housing design system. This type of capability is something that several generic BIM developers are currently exploring in R&D.

Higharc AI, currently in beta, uses visual intelligence to auto-detect room boundaries and wall types by analysing architectural features sketched in plan. In a matter of minutes, the software then creates a correlated model comprising all the essential 3D elements that were identified in the drawing – doors, windows, and fixtures.

Everything is fully integrated with Higharc’s existing auto-drafting, estimating, and sales tools, so that construction documents, take-offs, and marketing collateral can be automatically generated once the design work is complete.

In one of the demonstrations we have seen, a 2D sketch of a second floor is imported, analysed and then automatically generates all the sketched rooms and doors, with interior and exterior walls and windows. The AI generated layout even means the roof design adapts accordingly. Higharc AI is now available via a beta program to select customers.

Marc Minor, CEO and co-founder of Higharc explains the driving force behind Higharc AI. “Every year, designers across the US waste weeks or months in decades-old CAD software just to get to a usable 3D model for a home,” he says.

“Higharc AI changes that. For the first time, generative AI has been successfully applied to BIM, eliminating the gap between hand sketches and web-based 3D models. We’re working to radically accelerate the home design process so that better homes can be built more affordably.”

AI demo

In the short video provided by Higharc, as seen below, we can see a hand drawn sketch imported into the Autolayout tool. The sketch is a plan view of a second floor, with bedrooms, bathrooms and stairs with walls, doors and windows indicated. There are some rough area dimensions and handwritten notes, denoting room allocation type.  The image is then analysed. The result is an opaque overlay, with each room (space) tagged appropriately, and a confirmation of how many rooms it found. There are settings for rectangle tolerance, minimum room areas. The next phase is to generate the rooms from this space plan.

We now switch to Higharc’s real-time rendered, modelling and drawing environment, where each room is inserted on the second floor of an existing single floor residential BIM model, where walls, windows, doors and stairs are added and materials are applied. This is simultaneously referencing an image of the sketch. The accurate BIM model has been created, combining traditional modelled with AI sketch-to-BIM generation.

What is Higharc?

Founded in 2018, Higharc develops a tailored cloud-based BIM platform, specifically designed to automate and integrate the US housing market, streamlining the whole process of design, sales, and constructing new homes.

Higharc is a service sold to home builders, that provides a tailored solution which integrates 3D parametric modelling, the auto creation of drawings, 3D visualisations, material quantities and costing estimates, related construction documents and planning permit application. AEC Magazine looked at the development back in 2022.

The company’s founders, some of which were ex-Autodesk employees, recognised that there needed to be new cloud-based BIM tools and felt the US housing market offered a greenfield opportunity, as most of the developers and construction firms in this space had completely avoided the BIM revolution, and were still tied to CAD and 2D processes. With this new concept, Higharc offered construction firms easy to learn design tools, which even prospective house buyers could use to design their dream homes. As the Higharc software models every plank and timber frame, accurate quantities can be connected to ERP systems for immediate and detailed pricing for every modification to the design.

The company claims its technology enhances efficiency, accelerating a builder’s time to market by two to three times, reducing the timeline for designing and launching new plots by 75% (approximately 90 days). Higharc also claims that plan designs and updates are carried out 100 times faster than with traditional 2D CAD software.

To date, Higharc has raised $80 million and has attracted significant investment and support from firms such as Home Depot Ventures, Standard Investments, and former Autodesk CEO Carl Bass. The company has managed to gain traction in the US market and is being used to build over 40,000 homes annually, representing $19 billion in new home sales volume.

While the company’s first go to market was established house building firms, it has used money raised to expand its reach to address those who want to design and build their own homes. The investment by Home Depot would also indicate that the system will integrate with the popular local building merchants, so self-builders can get access to more generic material supply information.  The company also plans to extend the building types it can design, eventually adding retail and office to its residential origins.

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It is very difficult to gauge the impact of AI and Machine Learning (ML) on any specific AEC discipline. It’s made even harder when the applications that claim to be AI are in fact several different computing automation strategies compiled together, with AI contributing the least. This will remain the case for a long time as developers of automation tools will use the best / fastest computational strategies for the right problem, and AI may well not be the centre or main contributor to the magic you will see before your eyes. However, it will increasingly come into the mix.

We first talked with Augmenta in October 2022. At the time, the company didn’t have a shipping product but was making noises about the automation of building systems such as mechanical, electrical and plumbing (MEP). Many of the team, based in Toronto, Canada, were ex Autodesk-developers who were responsible for the generative design code that was impressively applied to Autodesk’s manufacturing-focused CAD tools – Fusion and Inventor. They had big ideas as to how generative design could be applied in AEC but couldn’t find any takers within that Autodesk division, so set up Augmenta and started working on the problem of building services.

I dream of wires

Augmenta has ‘shipped’ its first cloud product, aimed at wiring up electrical components in BIM models. The software is currently limited to USA projects, as the team has coded in US standards first. It automates the routing and coordination of conduit systems across an entire building, through multiple floors based on user-defined rules, with the specification of device and panel locations, no-go zones and run schedules.

When the BIM (Revit) model is uploaded, Augmenta wires up all the electrical components, puts holes through walls for conduit, models everything in 3D, and provides an estimated cost. It’s possible to run several strategies at once and compare the price difference. Typically, this would take an expert days or weeks. Augmenta does it in hours.

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Once the electrical design is completed this can be exported to Revit for editing, detailing and clash detection. As the solution is in the cloud, development is ongoing and will eventually expand out to other countries.

Augmenta is simultaneously working on its auto-plumbing application which may come out this time next year. It will be possible to run a solve for both wiring and plumbing simultaneously. Eventually, when MEP is ready, Augmenta could do the whole lot in one pass. However, obviously the results need to be checked by a professional and perhaps altered for reasons not yet built into Augmenta’s system.

Augmenta is one of the poster children for BIM 2.0. These expert systems, which are coming to assist professionals, rapidly crush specification and detail design times. HighArc is another example that has built an automated house detail designer and drawing production system for American residential house builders.

If you extrapolate what these applications can do today, ten years into the future, you should be able to realise that the AEC design space is going to look very different. The levels of industry knowledge that will be built into ‘intelligent’ software will mean smaller teams will become highly productive, with automation perhaps completing 90% or more of each discipline’s detail work.

Add in ten years development of conceptual AI design tools, autodrawings, digital fabrication strategies and we have to consider very different tech stacks and team skills within AEC. From an initial concept, it could be hours or minutes to get a fully detailed model, with drawings and costings.

Thoughts from the team

To coincide with the launch of the new product we had a wide-ranging talk with Francesco Iorio, CEO, Aaron Szymanski, co-founder and head of product, and Matthew Hernandez, VP of Growth.

Iorio spoke at AEC Magazine’s NXT DEV conference in 2023 (watch his presentation) and was part of our mainstage demonstration in 2024 showing a non-Revit BIM workflow (watch the presentation).

AEC Magazine: Why did you start with electrical?

Iorio: Electrical is actually the hardest from a technical perspective. It may be easier in the sense that electricity doesn’t fight gravity like water but other than that, from a purely computational geometric perspective, it’s by far the hardest because electrical systems are made from conduits.

The number of parts that need modelling are even ten times what you need for MEP. We aren’t aiming to output a diagram or a high-level conceptual model. We want to output everything, something that’s constructible. We generate miles of raceway in one shot. This isn’t an assistant, it’s an automated system.

To do plumbing we will have to consider pressure, easiest transitions. You need to consider pressure drops, differentials and we would have to do some simulation. And of course, you must think about slopes for drains, which are a constraint.

MEP, in terms of geometry and topology, is much easier if you think about it. There are these giant ducts that take up a lot of space but essentially with mechanical it’s all about performance. You need to think about flow rates, take into consideration thermal losses, noise, vibration. It’s something that would apply computational fluid dynamics too.”

AEC: What kind of benefit will Augmenta offer electrical engineers and Revit workflows?

Szymanski: At the moment we’re not producing 100% perfect designs. There’s still the need for human cleanup involved at the detail level. The system might add an extra bend here and there, and there could be a self-collision somewhere.

We’re tracking all this and reducing the incident of errors over time. Today we will get you that initial population, will get you from a model that has nothing, to something that has 80% and customers are going to have to clean up that model themselves. Over time the amount of human intervention will reduce. We kind of think of ourselves as like a self-driving car startup, trying to reduce the amount of human intervention.

We have a broad scope for electrical, we cover a lot of the details like supports and couplings and we will eventually detail that functionality providing a full BoM. We can wire up a small hospital in just three hours – that’s the whole design – and eventually we’ll get to the point where there’s effectively no clean up. That means zero to fully detailed BoM, fully coordinated, ready to build in just a couple of hours.

AEC: What data does Augmenta need to start modelling electrical systems?

Szymanski: Basically, there are three main inputs: Revit model with just the most basic setup in terms of electrical equipment, the conduit run schedule, which defines your ‘tos an froms’ and what you want to run between that equipment, along with the design rules.

We really want to be able to get to the point where it’s just requirements, just tell us how many lights are in the building, tell us what sort of loads are in different parts of the building, [then] we’ll place the panels, we’ll pick the equipment for you, we’ll route everything and coordinate everything.

Once we have the mechanical and plumbing, we just run all that concurrently, so you have a full MEP system, that’s fully designed and coordinated, just based off high-level requirements. We’re moving upstream.

Today the way that the industry works is by prioritising some systems over others, so mechanical has top priority, like ducting over plumbing. Ducting has priority over pressure piping and those together have priority over electrical, because electrical can go up down around etc. So electrical is effectively just routing around in whatever space it can find. It’s why we started there because we can solve that problem for them, without anyone having to be aware they’re using a radically new way of designing their systems. Once we get into mechanical and plumbing, we’re going to build those out all at the same time and at once solve the same problem in three or four hours.

We understand that iteration is an important part of the process and must support that feedback loop. Our system means you can go through two full iterations of the entire building in a day as opposed to that happening over the course of months. Now [at the moment] with each iteration it’s a whole rebuild, going from scratch every time which we know is not ideal, so we’re looking at user-defined iteration, whether users can lock parts of the design and keep everything else unlocked for the next iteration. But we want to be able for this to happen automatically. Every user might load in a new background model and the outcome is that everything is the same except for one duct. Our software should recognise that and assume everything else is optimal.”

AEC: We suspect that the makers of conduit, MEP and plumbing might be interested in Augmenta specifying their products?

Szymanski: We’re talking to the main providers of conduit, and there’s a bunch of conversations we’re having on that topic. The most straightforward interest is that they want to be included in our default library, they want to be in that end BoM.”

AEC: How will you sell Augmenta? Is it a SaaS service, on demand?

Szymanski: We have two ways to get to market at the moment. The first is we’re offering design services through a partnership with ENG. ENG is the largest big modelling firm in the United States. We are now running our software on their entire pipeline, so they are still kind of the front end from a consulting model. With the back end of it we do the solution generation, make the user models and do the final editing on top of them and deliver those to our customers, but we’re not expanding that right now.
We have been using our software long enough to have actually built buildings. Our first was an elementary school in Michigan, we have another school where construction kicks off soon too. We are just building a flywheel for ourselves, where we’re running on as many live projects as we can, learning and building a product to produce real value for the industry.

AEC: How does Augmenta use AI/ML?

Szymanski: We use a combination of methods; our solution generation pipeline combines machine learning and various algorithmics methods in different parts. Right now, from a machine learning perspective we’re training primarily not on customer sites or in customer models, but on our own models. So, for example, when we generate a design, we generate a number of designs, a user picks one of those and we record that preference. We start to learn what is a higher quality design and what is a lower quality design, and then obviously there’s where those additional edits are made.

We learn from those edits as well and all this is about driving internally a higher quality score reducing the amount of human intervention. So, there’s a machine learning component that’s really about fine-tuning our internal weights and biases to produce higher quality results. The other thing we are using machine learning for is site interpretation. We’re not training on customer sites, but we’re really interested in using machine learning to interpret the sites to understand what’s a bathroom, what’s an elevator shaft, so we’re starting to extract that context data sites so that there’s less and less human setup required at the front end.

The biggest challenge we face, and it’s no surprise at all, is how messy Revit models are. They are so inconsistently modelled; people are not using the right elements and categories correctly. It’s no surprise to anyone I know in the industry, but it causes a massive headache when you’re trying to programmatically make sense of that site.

We’ve done a pretty good job of that but there are still specific things that need to be defined like firewalls, as we shouldn’t be cutting through them.

AEC: To come out with a good solution for a design, you really need to make sure you have a good quality BIM model to start with. From talking with others, the rather random nature of quality and precision in architect’s BIM models is a problem.

Hernandez: Unfortunately, too many people don’t see the value in model fidelity or use the best practices and that’s pervasive in the industry. We see the way of changing this is by making people incredibly more efficient than they are today by automating and optimising the design.

When you control the design, you control virtually all the downstream workflows, even to the level of construction and constructability. We don’t want to just speed people up – that’s just one benefit – we literally want to transform the construction industry and change the 25 years of no productivity gains that has plagued our industry for many reasons.

When you control the design, which is essentially the data from which everything else flows, you unlock advances in estimation, in procurement, in construction in safety on the job site, because you can prefabricate more. We are partnering with people who model well, are into prefabrication, and are willing to spend time to use our innovative tool fully so they’re the ones who are going to become more efficient. We already know there’s a huge labour shortage gap in construction, let alone to get into BIM VDC.”

AEC: When people think of AI in AEC, they tend to think of Midjourney. How is Augmenta different?

Hernandez: Right now, customers are saving about 60% but that’s just literally the tip of the iceberg. We’re working on some incredibly hard challenges – geometry, clashes coordination, path choosing, all in 3D. This is very different to the LLMs of text-to-render.

And a lot of the solutions that are built for construction today only address the symptoms not the problem. Ultimately, multidisciplinary firms using this technology will be able to estimate within a percent, which is incredible, which makes that available to the rest of construction – lowest carbon emissions, embodied carbon lowers construction costs etc.

Business model

All this automation could transform the way AEC firms work, but it also has a huge potential impact on software firms. To date, software firms have charged per user per licence, or token-based systems for usage estimated over years. Automation systems are going to remove the need for many licences of software that firms rely on today. And yes, while others will say there will be new jobs, the correlation between automation and smaller design teams, and fewer licences is going to have real consequences in future decades. We asked how software firms like Augmenta are reinventing the software business model?

Hernandez: Our product is still very, very early. It’s not our goal to go out and sell a bunch of SaaS licences. The pricing model that we use is to measure as closely as we can how much value customers are getting out of our software. For example, like the 60% increase in speed, with routing, modelling, coordination and then we take a small percentage of that as a fee. So as our scope increases, our fee will increase, but we would still be leaving the user, whether that’s a services firm, an engineering firm, or contractor with 90% of the value that we create.

Conclusion

Augmenta is a fascinating addition to the design landscape and the team is taking on some very big challenges – not just by discipline, but by country; not just attacking incumbent systems and workflows, but by having to trial new business models.
We hope the team’s opinions have stirred some thoughts and ideas of your own, as to what is coming down the pipeline for this industry with automation. The key takeaway seems to be that for the AEC industry to fully reap the downstream benefits of these advancements, it must significantly improve the quality of its BIM models.

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These back-to-back conferences continue to be a focal point for technology in the AEC sector, and a unique oppportunity for AEC professionals and software developers to chart the course of next generation BIM software.

“After 20 years of ‘BIM as a means to produce drawings’, we are entering a new era, where design software will actually aid the design process and automate repetitive tasks and deliver real productivity benefits. NXT BLD and NXT DEV brings everyone together to influence and guide that development and connect the agents of change,” said NXT BLD / NXT DEV event director, Martyn Day.

“We must break down the silos, disrupt the old workflows and rethink what BIM could be. Technologies like the cloud, expert systems, DfMA, autodrawings and of course AI/ML are going to impact company tech stacks, workflows, data management, collaboration, data lifecycle.

“Software companies face business challenges as licence-count sales will be replaced by intelligent services and automation. NXT BLD and NXT DEV is the forum for that discussion.”

Tickets for NXT BLD and NXT DEV will be available in the new year.

Meanwhile, the presentations from NXT BLD and NXT DEV 2024 are available to view on AEC Magazine’s dedicated video website – www.nxtaec.com

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

Subscribe to the digital edition free + all the latest AEC technology news in your inbox, or take out a print subscription for $49 per year (free to UK AEC professionals).

Augmenta’s productivity promise
Augmenta has a brand-new product that uses AI to help electrically wire up a BIM model of a building in hours

Gen AI:  are you risk-managed ready? 
May Winfield explores the legal aspects of AI. How reliable is the information we get back? How confidential is the data we send? And what about copyright?

Finch untethered 
Architects spend a lot of time designing and revising floorplans. Finch aims to reduce preliminary design time by 80%

Navigating the boom in generative design
How can AEC professionals choose the right tool to meet their needs?

Autodesk charts its AI future 
If 2023 was the year that Autodesk announced its ambitions for AI, 2024 was when it fleshed out some of the details. But there’s still a long journey ahead

Graphisoft accelerates development
With a new CEO, and an ever-broadening product suite, Graphisoft is focussed on extending its global footprint

Bentley: the promise of data freedom 
When it comes to openness, few inspire as much confidence as Bentley in ensuring that customers retain control of their data

BHoM – addressing interoperability
This computational development project allows AEC teams to improve project collaboration and foster standardisation

Speckle matures
Speckle is replacing clunky file-based processes with powerful workflows that connect incompatible BIM tools

HP Build Workspace
HP’s new cloud platform boosts collaboration, streamlines site reporting, and uses AI to vectorise drawings

Dalux ‘Hygge’
Dalux has built a broad platform to liberate design and construction data

Matterport goes Pro
Matterport is lowering the barrier to entry for reality capture, bridging the gap between laser scanners and photogrammetry

DraftSight 2025
At time when 2D CAD is in the spotlight, we look at this AutoCAD alternative from Dassault Systèmes

The post AEC Magazine Nov / Dec 2024 Edition appeared first on AEC Magazine.

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

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