Pierre Bonnet

Founder of Engage-Meta
Building Semantic Processing Units (SPU) for profitable AI
Data Architecture • ODS • Semantic APIs • Enterprise AI

[Pierre Bonnet @piebonnet]

It would be unfortunate to miss the opportunity of enterprise-scale AI transformation because of methodological misunderstandings. See on LinkedIn: https://t.co/a4tpf1UohX

[Pierre Bonnet @piebonnet]

Read here (LinkedIn): https://t.co/Ta9xrL3p2Z

[Pierre Bonnet @piebonnet]

BEFORE DEPLOYING AI AT SCALE, companies must progressively build their cognitive asset. This is the purpose of the AI-augmented conceptual and logical data modeling approach developed by Engage-Meta and its SPU (Semantic Processing Unit) modeling agents.

[Pierre Bonnet @piebonnet]

Enterprise AI must progressively build 3 foundational Semantic Layers: - #03 — Business Vocabulary - #04 — Shared Business Semantics - #05 — Operational Semantic Layer (ODS + Semantic APIs) These 3 editions are designed to be read in sequence: https://t.co/zcaK8OStKE

[Pierre Bonnet @piebonnet]

Enterprise AI should not remain permanently dependent on expensive LLM inference.

The real objective is to transform LLM OPEX into semantic CAPEX through Operational Semantic Distillation (OSD).

LinkedIn post: https://t.co/zlrC5KqemH

[Pierre Bonnet @piebonnet]

Ontologies are AI illusion.
RDF, OWL, GraphRAG, and vector databases does NOT create a unified semantic architecture. Without an Operational Data Store (ODS) and shared conceptual models, enterprises simply accumulate fragmented semantic silos: https://t.co/tOe4ZQYmwT

[Pierre Bonnet @piebonnet]

Comment modéliser vos données pour réussir vos IA ?
Des données mal définies engendrent des erreurs et hallucinations des IA. Pourtant, construire un modèle de données cohérent synchronisé avec un glossaire métier et des narratifs sémantiques reste difficile.