Clarifeye Funding News-Clarifeye Secures €4M In Pre-seed Round

Clarifeye has developed a GenAI-ready data layer that connects raw data, human expertise, and LLMs, enabling companies to create expert-level AI agents that reason like their top specialists. This is particularly valuable in fields like law, regulation, life sciences, and manufacturing, where critical knowledge often sits with a small group of experts—scarce, slow to develop, and difficult to scale.

While current GenAI tools like RAG and LLMs can answer general questions, they fall short on traceability, consistency, and expert-level judgment. Knowledge remains fragmented across documents, databases, and individuals, leaving organisations unable to replicate expert reasoning reliably.

Clarifeye closes this gap by allowing developers and domain experts to co-design and refine LLM-powered workflows using proprietary, validated data. Experts can embed their reasoning methods and decision-making patterns directly into the platform, transforming informal know-how into operational intelligence for AI agents.

The outcome: domain-specific agents that deliver deeper reasoning, higher accuracy, and consistent performance, approaching that of human experts. Rolled out case by case, these agents create collective intelligence at scale, freeing specialists to focus on collaboration and innovation.

CEO Mathieu Grisolia notes that many GenAI projects fail because companies must choose between fast but shallow generalist AI and rigid vertical AI—neither of which captures the true depth of expertise. Clarifeye aims to change that.

Clarifeye takes a different path: we capture what is in your experts’ minds, turning it into an always-on AI that thinks like them, delivering ultra-specialised AI out of the box. This funding enables us to grow our team and accelerate that vision.

Clarifeye will use the fresh capital to advance its platform and expand its team with top talent.

About Clarifeye

Founded in 2025 Clarifeye is building a GenAI-ready knowledge warehouse that transforms unstructured content and expert know-how into structured, reusable data. By combining LLMs with subject-matter expertise through GraphRAG, it enables enterprises to create reliable, cost-efficient AI workflows and domain-specific agents that mirror expert reasoning at scale.