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The Latvian startup wants to help engineering teams keep track of requirements, testing, variants, and changes as products become faster and more complex to build.
Trace.Space has launched Trinity, a hardware engineering platform aimed at helping robotics, aerospace, automotive, defence, and other hardware teams move from prototypes to products manufactured at scale. Trinity connects requirements, testing, design parameters, and product variants in one system, while giving AI agents access to that engineering data.
Trace.Space was born from a problem Latvians Janis Vavere and Karlis Broders had already spent years trying to solve: engineering information was fragmented across requirements-management tools, spreadsheets, documents, and other systems, making it increasingly difficult to track how a change in one part of a complex product affected everything else.
Vavere saw the problem from companies buying and adopting requirements-management systems at Jama Software, while Broders saw it from inside the enterprises implementing them.
They came to the same conclusion: adding another feature to existing tools wasn’t enough. The underlying way engineering information was structured and connected had to change.
The launch marks a significant expansion for the Latvian startup, which began with requirements management and has since moved into testing, design parameters, variant management, and AI-assisted engineering.
I spoke with CEO and co-founder Janis Vavere to learn more.
Trace.Space was founded in 2022 in Riga and has offices locally and in the US.
From requirements management to Trinity
I first came across Trace.Space in 2023, when the company was focused on reinventing requirements management — the starting point for engineering data. Products begin with requirements defining what they must do and how they must perform, before evolving into new generations, versions, and variants.
“So even though it might not seem obvious at the beginning, requirements are where it all starts and where the core of the IP is held,” explained Vavere.
“We started in requirements management because we knew this from our past experience, and we wanted to hold that core.
But the plan was never to stay in requirements only. It was to expand across the engineering and manufacturing lifecycle.”
Trace.Space ultimately aims to manage as much as 80 per cent of the product development lifecycle. The company estimates that Trinity covers around 40 per cent today, up from roughly 10 per cent when Trace.Space started.
Beyond requirements and test management, Trinity is adding design parameter and variant management, alongside infrastructure and deterministic rules that allow AI agents to navigate engineering data and assess the impact of changes.
Over the next year, Trace.Space plans to move further into modelling, simulation, and manufacturing, including bills of materials, ERP, and supply chains.
Vavere ultimately envisages engineering data being connected across companies and their suppliers, rather than exchanged through documents.
“The current data exchange between suppliers and supply chains involves weeks of exchanging PDFs. We believe agents will be traversing these graphs, and Trace.Space will be the core that holds that engineering information.”
That expansion comes as investment in the industries Trace.Space serves is accelerating — but Vavere argues that engineering infrastructure has not kept pace.
Building is getting faster. Scaling isn’t
Investment across industries associated with physical AI is hitting record levels. Robotics companies raised $18.8 billion in the first half of 2026, already more than the $15 billion raised across all of 2025. Defencetech companies had raised $14.6 billion by early June, while spacetech companies raised more than $12 billion in 2025. Autonomous vehicle startups had already raised a record $21.4 billion by mid-April 2026.
However, turning that capital into products at scale is harder. Teams must move from a handful of prototypes to manufacturing fleets, variants, and regulated products.
Much of the engineering infrastructure behind those products still relies on spreadsheets, static documents, legacy platforms, and knowledge trapped in people’s heads. This becomes increasingly difficult as software-defined machines generate field data that engineers feed back into the next generation of products.
Trace.Space works with engineering teams including Lucid Motors, Serve Robotics, Xiphos, TMAP Mobility, and StandardX.
“The constraint is no longer whether ambitious teams can get funded,” said Vavere.
“It’s whether they can engineer, validate, and manufacture products fast enough to win.
Until now, the infrastructure required to move with that speed and precision existed only inside a handful of the world’s most advanced hardware companies, built and maintained by dedicated internal engineering teams. Trinity democratises that capability.”
Trinity connects requirements with tests, design parameters, and product variants. Rather than creating another copy every time a company builds a new vehicle, robot, satellite, or configuration, teams define what is shared and where each version differs. When something changes, Trinity shows what else is affected. That same connected structure is what makes AI agents useful in engineering.
Building for an agent-first engineering world
Trace.Space argues that its architecture allows it to do things that weren’t possible when earlier engineering platforms were built.
The platform is API-first, allowing developers to connect it with their existing development and test environments. It is also built around a configurable graph capable of accepting different types of engineering data, with native support for design parameters and variant management.
Trinity is also designed for AI agents. Vavere said customers are already using their own agents alongside the platform:
“They are also using Trace.Space as a harness, working through its API and running agents — including their own — to perform analysis.”
When faster prototyping creates more engineering risk
On AI-enabled hardware acceleration, Vavere said the impact is especially pronounced in software-defined machines and software-defined hardware.
“Teams can now ideate and build prototypes much faster than ever before, with the same number of engineers or even smaller teams. They can build the core hardware product and then explore different options, versions, and tests using software environments.”
AI is enabling teams to generate code faster, integrate it into modelling, simulation, and CAD environments, and rapidly test different versions of a product. That makes it easier to prototype, deploy early versions, and feed test and field data back into development.
But Vavere argues that this acceleration creates another problem: traditional systems engineering struggles to keep pace with the volume of data generated by increasingly rapid development cycles.
“Every release compounds risk because systems engineers can’t keep up with the amount of data. Teams no longer really know whether they are building the right product.
Risks compound, quality can drop, and requirements can be missed — potentially resulting in shipping the wrong product to the customer.”
This is where Trinity is intended to come in. As the number of prototypes, variants, and products in the field increases, the platform is designed to analyse the resulting engineering data and track whether requirements continue to be met. Vavere argues that this becomes increasingly important as companies move from prototypes to potentially millions of units in the field.
From AI agents to physical AI
In February, Trace.Space launched Space Agent, its agentic AI tool for systems engineering. Trinity extends that approach across a broader part of the engineering process.
Vavere said the company has heard from customers who previously worked at SpaceX that Trinity resembles the way they worked with engineering data there.
“That means we have built and productised a platform that some of the best companies in the world have only managed to create by putting their best engineers on internal tooling.”
Trace.Space is betting that as these machines move from prototypes into wider deployment, the engineering systems behind them will need to scale too. Trinity is the company’s answer to that challenge: as AI makes it faster to build and test hardware, the next bottleneck may be keeping track of what is being built, what has changed, whether it works, and how to manufacture it at scale.