UK Startup Embedd Secures €2.3 Mn to Power the Future of Physical AI

The company also plans to expand its work with semiconductor companies. The funding round was led by Seedcamp, with participation from Cocoa, Connect Ventures, 2100 Ventures, Vesna Capital, U.ventures, Underline Ventures, Common Magic and Roosh Ventures.

Michael Lazarenko, co-founder and CEO of Embedd, said, “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. We built Embedd to address exactly that, and we’re thrilled to have the backing of Seedcamp as we scale.”

Lazarenko added, “The promise of physical AI is enormous, but today’s hardware fragmentation is slowing innovation. This funding enables us to expand our platform and help more semiconductor companies bring their devices into emerging software ecosystems”

Embedd was founded by Ukrainian tech entrepreneurs Michael Lazarenko, Maxim Gorinov and Valentin Gololobov. The founders started the company after their previous hardware business faced major challenges, including chip shortages during COVID-19 and the impact of Russia’s invasion of Ukraine.

The founders noticed a common problem while working with hardware. Whenever they had to use a new chip, they often had to rewrite the software to make it work. They believed this problem would become even bigger as AI moved into physical machines and devices.

Embedd says it uses digital twins and AI agents to make it easier for semiconductor companies to connect their chips with software. The company believes every smart machine faces a similar challenge because its software needs to communicate with many different chips, and these chips often work in different ways.

Embedd aims to automate this process. Instead of engineers spending hours reading large amounts of chip documentation and writing code manually, the company creates a digital version of the hardware. Its AI agents then use this information to understand the chip and generate the code needed to connect it with the software.

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