We just raised €4M for Clarifeye.
We all had that first ChatGPT moment — the glimpse of what the future could be. Workers everywhere are already using GenAI for small tasks, and companies rushed to pilot larger deployments. But reality hit: a recent MIT study found that 95% of enterprise GenAI pilots failed to reach production.
Why? AI lacks context. It doesn’t know how your team works, interprets data, or applies tacit know-how to solve problems.
Today we are announcing a €4 million funding round by top investors to further the Clarifeye mission of closing the knowledge gap of GenAI.
The knowledge gap
When AI is asked surface-level questions, it looks magical. But when it faces tasks requiring domain expertise — interpreting regulations, applying clinical guidelines, or navigating industry-specific nuances — it breaks. The answers sound credible but miss critical context, eroding trust and creating real business or safety risks.
That’s because most approaches treat this as a search problem — better embeddings, better chunking, better retrieval. But expert decision-making isn’t just about finding the right page in a manual. It’s about pattern recognition, exceptions, and reasoning developed over years of experience. Traditional RAG can’t capture that.
Our approach
At Clarifeye, we believe that no matter how smart AI gets, you still need to teach it — just as you would with a new colleague.
Clarifeye empowers experts to structure their domain knowledge into AI-readable systems. The underlying is a GraphRAG architecture where subject matter experts guide the instantiation and knowledge structuring process.
The output is what we call the knowledge server that can retrieve precise information and apply appropriate reasoning methodologies to generate expert-quality responses throughout any AIs in your enterprise.
Key technical advantages
By capturing expert knowledge, we are able to interpret data much more efficiently, Our approach delivers roughly 10x better token efficiency because we retrieve contextually relevant information rather than broad document segments. Every response provides complete traceability that maps back to source materials and reasoning paths, which enables validation and refinement of the system. Domain specialists maintain expert oversight and control over how their knowledge is structured and applied. Most importantly, organizations can provide expert-level responses at scale without creating bottlenecks around key employees.
Why us
We’ve done this before: as serial entrepreneurs and former founding execs at Dataiku, we know how to take complex AI products to global enterprise markets.
With this €4M funding round led by EQT Ventures, Drysdale Venture, Station F and backed by strategic angels including Olivier Pomel (Datadog CEO) and Jean-Luc Robert (former Kyriba CEO), we’re ready to scale faster, bolder, and further.
Moving forward
We believe the future of companies is people and AI working side by side — learning from each other and improving together.
With Clarifeye, organizations can finally unlock the promise of AI in expert domains: contextual, trustworthy, and production-ready.
If you’re building GenAI systems that need to handle real domain complexity, or if you’re tackling knowledge representation challenges, we’d love to connect and compare approaches.