Embedd:Ukrainian founders building infrastructure for physical AI and a new generation of hardware products

Embedd is a deep tech startup founded by Ukrainian entrepreneurs Michael Lazarenko, Maxim Gorinov and Valentin Gololobov. The company is developing a software infrastructure for physical AI.

In 2026, Embedd attracted pre-seed investments to develop the platform and cooperate with semiconductor companies. The round was led by Seedcamp with the participation of several international investors.

The idea for Embedd arose from the practical experience of its founders in the hardware business. Their previous hardware venture faced supply chain problems, a shortage of chips, and the need to constantly rewrite software for new components. This experience helped the team identify a systemic problem: Physical AI requires better software infrastructure to work with hardware.

The target audience

Embedd's practice is focused on participants in the deep tech and hardware ecosystem:

• semiconductor companies;
• hardware startups;
• robotics companies;
• teams that create physical AI products;
• embedded software developers;
• devices manufacturers;
• R&D teams;
• industrial companies;
• companies working with sensors, edge AI and autonomous systems.

This is not a mass consumer product, but an infrastructure solution for teams that develop complex technological products.

What problem does practice solve?

Embedd solves the problem of complex and slow integration between software and hardware.

When a team creates a robotic device, sensor-based system, embedded product or autonomous hardware, it often depends on specific microchips and components. If a single component changes, the software may require significant rewriting.

This creates several problems:

• increased development time;

• heavy dependence on supply chains;

• difficulty switching to alternative microchips;

• high costs of hardware adaptation;

• slower time-to-market for products;

• technical barriers for teams that want to build physical AI products.

Embedd aims to reduce this friction between hardware and software.

How the solution works

Embedd creates a platform that helps simplifies the integration of microchips and software for physical AI products.

Its goal is to make the process of working with hardware less fragmented and more manageable. For teams, this means the ability to quickly adapt the software to different components, reduce dependence on specific hardware, and speed up development.

It is especially important that Embedd works not only with startups, but also within the semiconductor ecosystem. This allows the company to build an infrastructure product for a market where integration between chips, embedded software, and AI is becoming increasingly critical.

Why it is a good practice

Embedd can be seen as a good practice for several reasons.

• The company operates in a complex deep tech field that requires high engineering expertise.

• The startup solves a fundamental infrastructure bottleneck rather than a superficial problem, which can influence the development of the entire physical AI market.

• The case demonstrates how Ukrainian founders can create products for the global semiconductor and AI ecosystem.

• The team transformed their own negative experience from a hardware supply chain into the basis for a new product. This is a good example of a strong problem-founder fit.

In short, Embedd shows that Ukrainian entrepreneurial and engineering expertise is highly relevant in the most complex segments of the technology market: AI infrastructure, embedded systems, robotics, semiconductor software, and hardware integration.

The expected impact

The potential impact of Embedd can be significant if the platform becomes part of the infrastructure for physical AI.

• For hardware startups: faster product development and fewer technical barriers.

• For semiconductor companies: helping make their chips easier to integrate into AI-driven physical products.

• For robotics and industrial AI teams: reduced time from prototype to a working product.

• For the Ukrainian startup ecosystem: demonstrating that Ukrainian founders can compete not only in consumer apps or SaaS, but also in complex infrastructure and deep tech niches.

Potential for scaling

Embedd has strong scaling potential because physical AI is a global market. Robots, autonomous devices, smart factories, transport systems, industrial automation, and edge AI will require improved software and hardware integration.

Scaling can occur through:

• partnerships with semiconductor companies;

• integration with embedded software ecosystems;

• collaboration with robotics startups;

• support for industrial AI products;

• development of specialized tools for hardware developers;

• access to the markets of Europe, the USA, and Asia.

If AI is increasingly moving into the physical world, the need for such infrastructure solutions may increase.

Basic elements of practice

• Name of Practice: Embedd
• Practice Type: Deep tech infrastructure for physical AI
• Country of origin of founders: Ukraine
• Sphere of activity: physical AI, embedded software, semiconductor infrastructure, hardware integration 
• Targetaudience: semiconductor companies, robotics teams, hardware startups, embedded developers, industrial AI teams
• Problem: Hardware fragmentation and the complexity of software integration with microchips
• Solution: Software infrastructure that simplifies the integration of chips and software for physical AI products
• Focus area: Embedded systems, software engineering, AI infrastructure, hardware integration, semiconductor ecosystem
• Market Approach: B2B infrastructure, partnerships with semiconductor companies, developer tools
• Key result: a pre-seed funding round in 2026 for platform development and cooperation with semiconductor companies

Embedd is a strong example that demonstrates how Ukrainian founders can build an infrastructure deep tech product for a new stage of AI development.

The startup works at the intersection of physical AI, hardware, embedded software, and the semiconductor ecosystem. Its example shows that the future of AI depends not only on models and applications, but also on the infrastructure that enables artificial intelligence to work in the physical world.

For StartAid Ukraine, Embedd is a relevant case about advanced digital skills, engineering entrepreneurship, and the international scaling of Ukrainian technology founders.