As hardware development cycles accelerate, engineering teams face a growing challenge: tracking requirements, testing outcomes, design variants, and product changes across fragmented systems. Trace.Space, founded by Latvians Janis Vavere and Karlis Broders, has launched Trinity to address this bottleneck by consolidating engineering information into a single platform designed for robotics, aerospace, automotive, defence, and other hardware sectors moving from prototype to mass manufacture.

The problem that sparked Trace.Space's creation was straightforward but pervasive. Vavere encountered fragmented engineering data while working at Jama Software, where companies struggled to adopt requirements-management systems. Broders witnessed the same challenge from within enterprises implementing such tools. Both recognized that simply adding features to existing platforms would not solve the underlying structural issue: engineering information needed to be fundamentally reorganized and interconnected.

Founded in 2022 in Riga with offices in Latvia and the United States, Trace.Space has evolved significantly since its initial focus on requirements management. Trinity represents a major expansion, incorporating testing, design parameters, variant management, and AI-assisted engineering capabilities.

From requirements management to Trinity

Requirements form the foundation of any hardware product, defining functionality and performance before a design evolves into multiple generations, versions, and variants. According to Vavere, "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."

When Trace.Space began, the company deliberately chose requirements as its entry point. However, expansion was always the strategy. Vavere explained: "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 aims eventually to manage approximately 80 per cent of the product development lifecycle. Trinity currently covers around 40 per cent, a significant jump from the roughly 10 per cent the company addressed at launch. Beyond requirements and testing, the platform now handles design parameters and variant management, supported by infrastructure and deterministic rules that enable AI agents to navigate engineering data and evaluate the consequences of modifications.

Over the coming year, Trace.Space intends to extend further into modelling, simulation, and manufacturing domains, encompassing bills of materials, ERP systems, and supply chain management. Vavere envisions a future where engineering data flows seamlessly across companies and their suppliers rather than being exchanged through documents. He stated: "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."

Building is getting faster. Scaling isn't

Investment in physical AI and related industries is surging. Robotics companies raised $18.8 billion in the first half of 2026, already exceeding the $15 billion total for all of 2025. Defence technology companies had accumulated $14.6 billion in funding by early June, while space technology companies raised more than $12 billion in 2025. Autonomous vehicle startups reached a record $21.4 billion by mid-April 2026.

Yet converting capital into scaled manufacturing remains challenging. Teams must transition from prototype development to production fleets, manage multiple variants, and navigate regulatory requirements. Much engineering infrastructure still depends on spreadsheets, static documents, legacy platforms, and tacit knowledge. This dependency intensifies as software-defined machines generate field data that engineers incorporate into subsequent product generations.

Trace.Space works with engineering teams including Lucid Motors, Serve Robotics, Xiphos, TMAP Mobility, and StandardX. Vavere observed that the constraint has shifted: "The constraint is no longer whether ambitious teams can get funded. 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 integrates requirements with tests, design parameters, and product variants. Rather than duplicating data for each new vehicle, robot, satellite, or configuration variant, teams specify shared elements and define where versions diverge. When a change occurs, Trinity identifies all affected components. This interconnected structure also enables AI agents to operate effectively within engineering workflows.

Building for an agent-first engineering world

Trinity's architecture enables capabilities unavailable when earlier engineering platforms were developed. The system is API-first, permitting developers to integrate it with existing development and testing environments. It is built on a configurable graph that accommodates various engineering data types, with built-in support for design parameters and variant management.

The platform is designed with AI agents in mind. Vavere noted that customers are already deploying their own agents alongside Trinity: "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

AI-enabled hardware acceleration has particularly pronounced effects on software-defined machines and software-defined hardware. Vavere explained: "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 accelerates code generation, integration into modelling and simulation tools, and rapid product variant testing. This facilitates faster prototyping, earlier deployments, and quicker feedback loops incorporating test and field data. However, this acceleration introduces a new challenge: traditional systems engineering cannot keep pace with the data volume generated by accelerated development cycles.

Vavere cautioned: "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."

Trinity addresses this challenge by analysing engineering data and verifying whether requirements remain satisfied as prototype counts, variants, and field deployments multiply. This capability becomes increasingly critical as companies scale from prototypes to potentially millions of units in operation.

From AI agents to physical AI

Trace.Space launched Space Agent, an agentic AI tool for systems engineering, in February. Trinity extends this agent-centric approach across a wider engineering scope. Vavere shared feedback from former SpaceX employees: "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's thesis rests on the premise that as machines transition from prototypes to widespread deployment, their underlying engineering systems must scale correspondingly. Trinity represents the company's solution: as AI accelerates hardware building and testing, the next constraint becomes managing what is built, tracking changes, verifying functionality, and enabling scaled manufacturing.

Source: Tech.eu