Yannis Kopsinis is the co-founder and CEO of LIBRA AI Technologies, where he combines over 20 years of hands-on expertise in Machine Learning with a deep commitment to making AI practical, trustworthy, and impactful. He holds a PhD in machine learning for signal processing and has a career spanning both the theoretical and the commercial frontiers, always focused on delivering measurable value. Today, at LIBRA AI, he leads a growing team of experts, designing custom AI solutions that turn data into insight, and insight into action. We met with Yannis and talked about his LIBRA and his journey. Here's what he told us.

Tell us a bit about LibraAI — how did the idea come about, and what made you realize there was a market need for your solution?

The idea behind LIBRA AI emerged from years of working with Machine Learning at a time when it was still largely a research field. Completing my PhD in 2003, I had the opportunity to apply these methods early on across different domains, from signal processing and tele-communications to real-time analytics and medical imaging. Over time, it became clear that while AI was gaining momentum, many organizations still struggled to translate its potential into real-world value.

That disconnect became the motivation for founding LIBRA AI. We started by helping early clients explore specific use cases and quickly moved into building end-to-end solutions that delivered measurable outcomes. We aimed at bridging the gap between innovation and execution—to bring the benefits of AI into day-to-day operations in a way that was clear, practical, and aligned with each client’s goals.

When we expanded to Greece, we found a business landscape that was evolving quickly, especially as the pandemic accelerated the need for digital tools and data-driven decisions. Companies across sectors were more open than ever to exploring AI, not just as a trend but as a practical solution to real challenges. We were fortunate to support them, not only with technical depth but with a flexible, collaborative mindset focused on delivering lasting value.

What has characterised LIBRA from the beginning is that we don’t follow the AI hype; we build what works. From day one, the goal was to democratize AI and make it usable, explainable, and truly valuable for the organizations that need it most.

What sets LibraAI apart from similar solutions in the market? What specific value do you deliver to your customers and partners?

At LIBRA AI, what sets us apart is our ability to design AI solutions that are fully adapted to each client’s unique context. We build upon solid technologies but go far beyond plug-and-play tools, focusing instead on deep customization and system-level integration. The result is not just a functional model, but a solution that fits into the client’s real-world operations and delivers sustained business value with optimised operational costs.

This approach allows us to move beyond one-size-fits-all thinking. We work closely with our clients to ensure that every system we deliver aligns with their strategic priorities, operates smoothly within existing environments, and continues to evolve over time.

This is made possible by the strength of our team. LIBRA brings together experienced strategists, engineers, and data scientists, people who combine technical depth with practical insight. It’s a culture that values experimentation, learning, and long-term impact, where solving meaningful problems takes priority over chasing trends.

What has been your biggest challenge so far — and how did you overcome it (or how are you addressing it)?

Keeping up with the rapid pace of AI while scaling has been our biggest challenge and one of our most defining. The field evolves constantly, and aligning that momentum with the specific needs of different industries demands not just technical skill, but agility and focus. We’ve addressed this by building a flexible, interdisciplinary team, people who not only stay ahead of the curve technically, but who can quickly apply that knowledge across sectors. We stay close to our clients throughout, making sure that what we build is both advanced and directly relevant to their goals.

R&D is a core part of how we work. Through large-scale European research projects, such as Horizon 2020, and continuous internal innovation, we test and apply emerging technologies in ways that make sense for real-world business settings. This combination of strategic research and hands-on exploration helps us refine our approaches, stay adaptable as we grow, and keep our clients ahead.

What are the most important lessons you’ve learned along the way?

One of the most important lessons we’ve learned is that successful AI adoption starts with alignment, not just on goals, but on understanding. Many companies are eager to embrace AI, but expectations can vary widely, especially when the technical complexity isn't always visible. We've learned to invest time upfront in helping clients see what AI really involves, what it can deliver, and how to distinguish between surface-level tools and deeper, more integrated solutions.

This has also taught us that qualification matters. Not every opportunity is the right fit, on either side. Saying no or reshaping a project before it starts can save time and ultimately lead to better results.

We’ve also learned that talent isn’t only about expertise, it's about the mindset. Our best work comes from people who are curious, collaborative, and willing to keep learning, because the field keeps changing. That’s why we’ve built a team culture where research and delivery go hand in hand, and where experimentation is part of everyday work.

Finally, timing is everything. You can have the right solution, but if the organization isn’t ready, technically or culturally, it won’t land. We’ve learned to recognize those moments when the conditions are right and to help our clients seize them.

With the global momentum around AI, how do you see Greece positioning itself in this space? What role can innovation-supporting institutions play, and how can companies better seize the opportunity?

Greece has a real opportunity to play a leading role in applied AI, but only if we move with purpose. The technical talent is here. We see it every day in our team and in the broader ecosystem: engineers, data scientists, and researchers who are as skilled as any in Europe. And we have sectors where AI can make a real difference: maritime, energy, manufacturing, logistics, and even public services. But talent and potential aren’t enough without the right structures around them.

In timing, alignment, and clear pathways to implementation, is where institutions can make a real difference. Funding pilots is important, yes, but even more critical is helping create environments where companies can test, iterate, and prove value safely and quickly. Opening access to data, supporting upskilling programs, and enabling real-world experimentation aren’t just supportive measures; they are prerequisites for growth.

For businesses, my advice is to stop thinking of AI as a future investment. It’s a present-day differentiator. The best way to start is small and focused: choose a use case where the value is clear, work with people who understand the technology deeply, and commit to learning as you go. AI isn’t a magic solution; it’s a tool. And like any powerful tool, it works best in the hands of people who know how to use it.

I’m optimistic about Greece’s AI potential, not in a headline way, but in a grounded, practical sense. We’re already seeing signs of it in the work we do across industries. If companies, institutions, and technology providers move forward together, I believe Greece can become not just a hub for AI talent, but a reference point for applied, human-centered AI done right.