Description

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Jérémy Ravenel, researcher, advisor, and founder of the open-source NaasAI platform. Known for bridging the gap between theory and execution, Jérémy shares his journey from Excel-heavy finance roles to leading cutting-edge research in AI interoperability and enterprise knowledge graphs.

Together, they explore:

• Why so many GenAI initiatives fail without semantic structure and context.

• What it means to “clean your room” before deploying AI agents.

• How ontologies can evolve from niche academic tools to practical building blocks of enterprise-scale AI.

• The tension between perfection and pragmatism in data work.

🎧 Tune in for a conversation on the hidden structures behind meaningful AI, and why the future might depend on how well we model what we already know.

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