What are you building, and why now?

We are building a deep tech platform that combines spectroscopy, the study of how matter interacts with light, with AI to turn complex spectral data into real-time, actionable insights. Our first application is in-ovo sexing, which allows hatcheries to determine the sex of a chicken embryo inside the egg at the earliest stage in the market and avoid the culling of male chicks entirely.

We looked at a range of different use cases very closely with customers. What stood out was a clear gap between what hatcheries had access to and what they actually needed: a solution that is non-invasive, cost-efficient, and easy to integrate into existing operations. Many existing approaches did not meet these requirements in practice. At the same time, developments in AI and data processing now make it possible to fully unlock spectral data at scale. This combination allows us to build a solution that works operationally today, while laying the foundation for a much broader technology platform.

‍What did you see that others missed?

Instead of starting with the technology, we focused on the problem first. We started understanding how hatcheries actually operate day to day. Most approaches in this space focused on biology alone, but this approach meant we focused on what it takes for a solution to be embraced by the customer and run at high throughput, without adding manual steps or operational complexity.

Spectroscopy has been around for decades, and it is non-invasive, scalable, and cost-efficient. Historically, it has been underutilised because the data was considered too complex to interpret at scale. With new AI techniques, we can extract meaningful patterns from these spectral signals and turn them into clear, actionable results. That’s what allows this to work in practice. Instead of building a single-purpose machine, we are building a platform that can interpret biological processes in real time.

‍What fundamental breakthrough or shift made this possible?

The fundamental shift started from the problem itself, not the technology. We have built a purpose-specific AI toolbox designed from the ground up for this challenge. The key enabler is time-based spectral data, combining spectroscopy with AI to turn the added complexity of temporal data into an advantage, unlocking insights that static approaches simply can't reach.

That toolbox is now maturing to a point where it can be deployed across different use cases, making the underlying technology broadly applicable beyond its original scope.

‍What needs to go right for this to work at scale?

This is a classic deep tech scaling challenge. We need to get three things right at the same time: technology, industrialisation, and go-to-market.

On the technology side, it’s about robustness across different environments. On the industrial side, it’s about integrating seamlessly into existing hatchery infrastructure.

And commercially, it’s about building trust with customers and delivering clear ROI.

We are already seeing this coming together. We have our first customer fully operational, additional systems in implementation, and a strong pipeline of future customers. The recent €10m Seed funding round allows us to scale all three dimensions in parallel.

‍How does the world change if you are successful?

If we are successful, chick culling becomes obsolete globally.

Beyond that, the food system becomes more precise and data-driven. Producers can make decisions based on real-time biological insights, instead of relying on averages. Hatcheries can know the sex of an egg and gain visibility into fertility, embryo health, and quality. Eggs that are identified early and non-invasively as non-viable can be redirected into applications like vaccine production, turning what was previously a loss into a new value stream. This reduces waste, improves efficiency, and makes production more sustainable.

And at the platform level, what we're really building is "spectral intelligence,” technology that extends into food quality/fraud, animal health, and beyond.

‍Why are you the team to do this?

We bring together deep technical expertise in AI, spectroscopy, and biology with real experience in building and scaling hardware-enabled software companies.

A large part of the team has built successful startups before; my co-founder, Paul, sold his previous company, ProGlove, for $500m, and we combine that with a strong focus on execution and customer collaboration. From the beginning, we worked closely with hatcheries, iterated quickly, and built the product around real operational needs. That is why our solution is technologically advanced and practical to deploy at scale.

We are currently a team of 20 driven people and are now expanding the team significantly following our recent funding round.

‍What does the next 12 to 24 months unlock?

The next phase is about scaling what we have already proven. We will bring our technology to more customers, expand into additional markets, and continue to improve performance and reliability. We are also building the organisational and operational foundation needed to support that growth, from team expansion to production capabilities.

In parallel, we will deepen the technology by unlocking additional insights beyond sex determination, developing AI-powered spectroscopy applications for new markets.