Discover how machine‑readable Data Product Contracts can transform your Data Mesh into an AI‑ready ecosystem. This session demonstrates the end‑to‑end journey from contract design to building an enterprise knowledge graph with Neo4j, enabling semantic interoperability, governance, and advanced AI use cases like RAG. Practical patterns, real‑world examples, and lessons learned included.

AI only scales with clear semantics and reliable data products. In this talk, I will show how Data Product Contracts (e.g. based on ODPS) as machine‑readable specifications can be used to derive an enterprise knowledge graph that in turn provides context knowledge for GenAI systems. We will walk through the end‑to‑end process: • Contract design (interfaces, SLAs, quality rules) • Automatic derivation of ontology or knowledge graph artifacts to form a semantic layer • Integration of the semantic layer into a self‑service platform • Impact on findability, usability, and governance

The result: data products developed across domains that provide context information to be fed into an enterprise semantic layer to form the foundation for AI use cases. Real‑world examples, best practices, and anti‑patterns complete the session.