The Story of Threedy: Building the Visual Computing Infrastructure for Industrial 3D

The Story of Threedy: Building the Visual Computing Infrastructure for Industrial 3D

The phone call came at the worst possible time. Christian Stein’s wife was about to give birth to their first child. His research team at Fraunhofer was becoming a company. And suddenly, he was responsible for transferring 20 employees, navigating complex legal restrictions, satisfying multiple stakeholders, and actually running a business—all simultaneously.

“I was actually becoming a father for the first time right at the same point in time,” Christian recalls. “So just overwhelming things to take care of and totally different from what you had expected, actually.”

Most startup origin stories begin with a founder in a garage, tinkering away at nights and weekends. Threedy’s story is different. It’s about what happens when a decade of research, early enterprise customers, and an entire department need to transform into a venture-backed company. In a recent episode of Category Visionaries, Christian Stein, CEO and Co-Founder of Threedy, shared how they built an industrial 3D platform by completely rethinking how 3D data should work in modern software.

From Games to Research to Company

Christian’s path to founding Threedy wasn’t linear. “I have a history as a computer scientist working a lot with computer graphics software engineering, did my first steps in the games industry and then by chance decided to master and joined the Fraunhofer research organization,” he explains.

At Fraunhofer, Christian worked on problems that most commercial software companies weren’t equipped to tackle: how to handle massive, complex industrial 3D data in ways that could scale across distributed systems. The work was theoretical enough to be interesting, practical enough to attract enterprise attention.

Then something unexpected happened. Companies started paying for it.

“We actually got the first paying customer roughly four years before we founded the company, which was also kind of the trigger to decide that, hey, we got something here,” Christian shares. “We should really think about what we can do with it outside of the research environment.”

That first paying customer validated something bigger than a research project—it suggested a fundamental market need that wasn’t being met by existing solutions.

The Complexity of Spinning Out

Unlike typical startups that grow gradually, Threedy’s launch was more like an organ transplant. The company had to extract an entire functioning department from a research institute while preserving existing customer relationships, contractual obligations, and intellectual property agreements.

“We start the company by transferring the full department of roughly 20 people into the company and then suddenly there are so many new things you need to take care of that previously the head organization had done,” Christian explains.

It wasn’t just about incorporation paperwork. There were legal frameworks to navigate, stakeholder interests to balance, and the fundamental challenge of transforming researchers into a commercial organization. “The first six months, I guess surprising. All the different things you need to take care of. There’s a few things that are very special about the company.”

The preparatory phase alone took roughly four years—”went through a lot of different programs before actually spinning out the company with all the, yeah, let’s say legal restrictions and all the different stakeholders that need to be satisfied.”

Rethinking 3D Data from First Principles

What makes Threedy’s technology unique isn’t incremental improvement—it’s a fundamental reimagining of how 3D data should work in modern applications.

Traditional approaches to industrial 3D data fall into two camps, Christian explains. Some systems “build software that kind of generates images on a server side and then streams images to the client, abstracting about data size distribution.” Others rely on “exporting it from complex management systems, by converting it, by reducing it to match the capabilities of an application of an end user device.”

Both approaches have limitations. Streaming images solves distribution but sacrifices interactivity. Converting and reducing data works for specific applications but creates data management nightmares at scale.

“Here’s exactly where we differ, that what we did roughly 10 years ago is starting to think about how we think that data should be utilized in a modern software world where data is typically progressively streamed based on user demands, based on interactions in the application,” Christian says.

The insight: if you assume constant connectivity and can distribute workload between client and server, you can redesign algorithms to work better than either traditional approach. “We can kind of redesign standard algorithms to work in an always connected world a little bit better than these approaches.”

The Market Shift Driving Demand

Threedy’s timing aligned with a fundamental change in how industrial companies use 3D data. “While in the past, maybe utilization of complex heavyweight industrial data was mostly all about engineering and design, today there’s a rising demand of utilizing that data all across the organization and downstream along, let’s say, the whole product life cycle of the physical products that are manufactured,” Christian explains.

3D data that once lived exclusively in CAD systems for engineers now needs to be accessible to sales teams configuring products, service technicians troubleshooting issues, and manufacturing operations optimizing production. This democratization of 3D data created demand for infrastructure that could handle enterprise-scale complexity while remaining accessible to non-technical users.

Learning to Sell What You Built

Having paying customers from the research phase created false confidence about go-to-market. The early innovators at German automotive companies who sought out Fraunhofer’s research weren’t representative of the broader market.

“Coming out of the institute, I think we thought that it’s going to be very easy to replicate that way of finding these people,” Christian admits. The reality proved much harder. Initial sales hires were “overburdened them with the complexity of talking to different ICPs, of delivering, let’s say the full story and kind of going into detail on all the different aspects and possibilities.”

The fix required years of iteration: “Over the first years now we kind of stripped it down into different core use cases, into sweet spots where we can deliver very strong values.” Rather than explaining everything their platform could do, they focused salespeople on “very specific topics, specific core customers.”

The Fundraising Lesson

Threedy’s Series A in December 2023 came with hard-learned lessons about timing. The company had a crucial decision point during the metaverse hype—when their industrial 3D and mixed reality platform was perfectly positioned—and chose to wait.

“We did probably a huge mistake by deciding to delay the fundraising for six months. While we are right in the middle of the metaverse hype, which obviously given our core topic, Threedy was right playing into our cards,” Christian reflects. “But then we missed the hype cycle and into the declining economy.”

The six-month delay meant navigating a dramatically different environment with constant recalibration “while at the same time the environment was drastically changing quarter by quarter.” His advice moving forward: “Looking forward the next time we’ll definitely try to prepare maybe and the decisions and process for a longer time and make sure that the timing is not so bad.”

Characteristically, Christian also managed to have his second daughter two months before closing the Series A. “I actually did the mistake twice,” he laughs, “because we raised the series A in December 2023 and two months before I got my second daughter. So I’m not learning from it.”

The Future: 3D Data as Ubiquitous as the Internet

Christian’s vision for Threedy extends beyond serving German automotive companies with better 3D tools. He sees a future where 3D data becomes as fundamental to computing as the internet itself.

“The same way that over the past two decades maybe the Internet has become an everyday tool that you use on every device and anywhere, we think that the same is going to be true for 3D data and especially also for mixed reality applications and AI,” he explains.

Threedy’s bet is on becoming foundational infrastructure for this future. “We see our technology there actually as a very unique foundational part on how interfacing with 3D data can work at scale for distributed industrial environment.”

The shift from file-based workflows to API-driven data spaces is central to this vision: “One of our core concepts of putting a data space into the center and moving away from files to APIs on shared spaces is something that is going to be very valuable in building these industrial applications of the future.”

With German automotive facing economic headwinds, Threedy’s 2025 strategy focuses on diversification—”win new logos outside of automotive and do our first internationalization” with customers in the US and India. They’re also expanding their partner network, enabling other companies to build next-generation 3D and mixed reality applications on Threedy’s infrastructure.

The company that began in a research institute, spun out with 20 employees during a personal life transition, and learned to commercialize complex technology is now positioning itself as the foundational layer for how industrial companies will interact with 3D data in an AI-powered, mixed reality future. Sometimes the most important infrastructure gets built by those willing to rethink it from first principles—even when the timing is overwhelming.