Ukrainian-Founded Embedd Raises $2.7M to Let AI Agents Write Their Own Chip Integration Code

Embedd, a London based startup building software infrastructure for physical AI, has raised 2.7 million dollars, roughly 2.3 million euros, in pre‑seed funding led by Seedcamp, with participation from Cocoa, Connect Ventures, 2100 Ventures, Vesna Capital, U.ventures, Underline Ventures, Common Magic, and Roosh Ventures.

The company was founded by chief executive Michael Lazarenko, chief product officer Maxim Gorinov, and chief technology officer Valentin Gololobov, three Ukrainian entrepreneurs whose earlier hardware company was first disrupted by global chip shortages during Covid and later by Russia's invasion of Ukraine. That experience repeatedly forced the team to source alternative components and rewrite integration software around them, a firsthand encounter with a problem the founders came to see as structural across the entire hardware industry rather than specific to their own supply chain troubles.

The problem Embedd is built to solve sits at a level most software teams rarely think about directly: every intelligent machine, whether a robot, a car, a drone, or a medical device, depends on dozens of chips that do not share a common language with one another. Engineers currently have to work through thousands of pages of technical documentation and hand‑write the integration code that lets those chips communicate with the software layer running above them, a process that Embedd says typically takes four to six months per project. That friction slows down development timelines across an entire wave of industries now racing to bring physical AI products to market.

Embedd's approach replaces that manual process with what the company describes as a digital twin of the underlying hardware. By modeling a chip's behavior and specifications in software, the platform gives AI agents the context they need to run the integration process themselves, automatically generating the code required to make each chip usable by the software layer above it, rather than requiring an engineer to read documentation and write that code by hand. According to the company, this approach has enabled customers to deliver production‑ready chip software up to six times faster than the traditional manual process.

Lazarenko has framed the underlying opportunity in terms of a much larger shift already underway across manufacturing, transportation, and healthcare. "The next wave of AI will power factories, vehicles, robots and critical infrastructure, but today, every change in hardware creates huge complexity for software teams and that friction is already massively slowing innovation," he said. "We built Embedd to address exactly that, and we're thrilled to have the backing of Seedcamp as we scale." He added that the promise of physical AI is enormous, but that hardware fragmentation remains one of the central forces holding back faster progress across the sector.

Seedcamp general partner Carlos Eduardo Espinal pointed to the scale of that fragmentation problem as the primary reason the firm chose to lead the round. "Embedd is tackling one of the fundamental challenges facing industries from robotics and manufacturing to healthcare and automotive, and we're excited to back Michael and the team as they build the infrastructure underpinning the next generation of intelligent machines," Espinal said.

Embedd has already secured a customer relationship with Microchip Technology, one of the world's largest semiconductor manufacturers, giving the young company direct commercial validation from within the chip industry itself rather than solely from the downstream robotics, automotive, and medical device companies its platform is ultimately built to serve. That kind of relationship also positions Embedd closer to the source of hardware fragmentation, since semiconductor makers themselves have a direct incentive to make their chips easier for software teams to adopt quickly, shortening the time between a chip's release and its appearance in commercial products.

With the new funding, Embedd plans to expand its platform's capabilities, deepen partnerships with additional semiconductor companies, and scale its technology to meet growing demand as investment in robotics and other physical AI applications continues to accelerate. The company's broader ambition is to become a foundational layer that reduces the time needed to bring new chips into products across the physical AI ecosystem, a goal that, if realized, would position Embedd less as a tool for any single industry and more as shared infrastructure sitting underneath the wider shift toward AI systems that operate in the physical world rather than purely in software.