I'm Jérémy Ravenel, a farm boy turned data mercenary and now entrepreneur, passionate about practical innovation to make our digital lives less chaotic.
I help people and companies create data & AI products that make work easier and more efficient.
My journey started in finance, where I discovered the real world was less automated than expected. Frustration led me to coding, Python, and Jupyter notebooks—and eventually founding CashStory, bridging business and tech teams across France.
Currently, I'm leading Naas.ai, building an open Universal Data & AI Platform to help organizations create powerful ecosystems of AI assistants. Our goal is to empower individuals and organizations to create their own, sovereign AI using ontologies as a unifying field between data, AI models, workflows, analytics, and external systems.
I'm also a Research Associate in Applied Ontology at the University at Buffalo, working to make AI systems more understandable and trustworthy. I advise companies on Data and AI strategy at Forvis Mazars.
This mix of research, hands-on development, strategy and finance is the intersection of philosophy, tech, and business I always dreamed of working on.
If you're curious about how data and AI can make your work easier and more impactful—let's connect!
📧 [email protected]
“Anyone should be able to turn their data into a valuable asset. I build bridges to make it happen.”
- AI isn’t magic—it needs clear structure and disciplined data management.
- Ontologies and knowledge graphs are essential for robust, trustworthy AI.
- The real work is in organizing, contextualizing, and connecting data—not just chasing the latest algorithm.
Insights:
- The Role of Ontologies in AI: Companies like Palantir use ontologies to integrate data, enable deeper insights, and deliver advanced, context-aware AI solutions.
- Maintaining Ontologies: Strategic balance, regular updates, domain expert collaboration, and clear documentation are key.
- Knowledge Graphs & AI: Without ontologies, knowledge graphs are chaotic. Structured ontologies give semantic depth and reliability.
- LLMs & Data Organization: AI’s value is limited by the messiness of business data—organization is critical.
- GraphRAG & Challenges: Data quality, retrieval, graph construction, LLM integration, scalability, and ethics are crucial for next-gen AI assistants.
Founder & CEO, Naas.ai (Apr 2023—Present)
Building a Universal Data & AI Platform using ontologies to unify data, AI, workflows, and analytics.
Senior Advisor, Data & AI Services, Forvis Mazars (Nov 2024—Present)
Advising on AI strategy and implementation, focusing on auditable, trustworthy solutions.
Research Associate, Applied Ontology, University at Buffalo (Nov 2024—Present)
Researching executable ontologies for AI alignment, explainability, and compliance.
Founder, CashStory (Nov 2016—Apr 2023)
Automation services for business professionals, education, and open-source products.
Group Cash Manager, Technip (Apr 2013—Oct 2016)
Treasury management, business systems, and cross-organizational strategy.
Education
- Stanford University Graduate School of Business
LEAD Program: Strategic Leadership, Critical Analytical Thinking, Financing Innovation, Lean Management (2016–2017) - MBS School of Business
Master’s Degree, Management—Corporate Finance: Working Capital & Cash Management (2009–2013)
Certifications
- Decision Intelligence (LinkedIn)
- Analyzing and Visualizing Data with Microsoft Power BI (Microsoft)
Skills
- Strategic Thinking
- Decision-Making
- Data Engineering
- Artificial Intelligence (AI)
- Applied Ontology
- Explainable AI (XAI)
- Cash Management
- Business Consulting
- Project Management
- Custom Software Development
Languages
- English (Native or bilingual)
- French (Native or bilingual)
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