Trace.Space has launched Trinity, an artificial intelligence platform designed to help hardware engineering teams manage complex product requirements, test coverage, and configuration changes as development cycles accelerate. The announcement addresses a growing challenge in robotics, aerospace, automotive, and defense where physical products and their software variants now evolve simultaneously in the field. Trinity is positioned as a tool that keeps engineers in control of what each product version contains, what has changed, and what has been tested.
Scaling Hardware Engineering for AI-Accelerated Teams
The platform expands what a single engineering team can build and manage by deploying specialized AI agents that review requirements, analyze modifications, and surface gaps across product generations and software releases. Teams can develop the next version of a product while continuing to improve variants already deployed with customers. As product families grow, Trinity maintains a structured record of what each version contains and what has been validated.
Serve Robotics, which operates a fleet of autonomous sidewalk delivery robots, is among the early customers using Trace.Space. According to Jackie Song, Senior Systems Engineer at Serve Robotics, the company runs multiple generations of robots and autonomy software in the field at the same time, each evolving on its own timeline. The main challenge, Song explained, is keeping requirements, tests, and hardware configurations connected across all those versions at once.
Building a Connected Engineering Foundation
Trace.Space was founded by Janis Vavere and Karlis Broders, who had previously spent years working on requirements management at Jama Software and inside enterprise implementations. They concluded that the core problem was not another missing feature but the underlying foundation of product data. Every complex product starts with requirements, and Trinity keeps those decisions connected to tests, design parameters, product variants, and the wider engineering system.
That connected structure is what makes AI agents useful in engineering, according to the company. Trace.Space launched Space Agent in February to bring agentic AI into systems engineering, and Trinity now combines three capabilities described as Record, Relate, and Reason. Teams record engineering truth in one place, relate requirements and variants as products change, and reason across those connections to identify impact and gaps.
Record Levels of Investment in Physical AI
The launch comes as capital flows into physical AI at record levels. Robotics companies raised US$18.8 billion in the first half of 2026, surpassing the US$15 billion raised across all of 2025. Defense technology attracted US$14.6 billion by early June, 52% above its previous record full-year total, while space technology companies raised more than US$12 billion last year and autonomous vehicle companies have raised US$21.4 billion so far this year.
Trace.Space argues that the constraint for ambitious teams is no longer access to funding but the ability to engineer, validate, and manufacture products fast enough to compete. According to Janis Vavere, Trinity makes advanced engineering infrastructure available beyond a handful of the world's leading hardware companies. The company works with engineering teams across robotics, aerospace, automotive, and defense, including Serve Robotics, Xiphos, TMAP Mobility, and StandardX.
Trace.Space, founded in 2022 in Riga, Latvia, with offices in San Francisco, has raised US$6 million from investors including Cherry Ventures, Charlie Songhurst, Tiny, Foreword, Illusian, Fiedler Capital, and Finn Murphy from Nebular. The company sells directly and through resell partners such as Systematics in Israel and SLEXN in South Korea. Trinity joins a growing category of tools aimed at giving hardware teams the same speed and precision that software teams have long taken for granted.