Topic Hub
Model business meaning explicitly with ontologies so agents, analytics, and NL interfaces stay grounded and consistent.
Entity, relationship, and taxonomy design
Ontology-to-schema mappings for reliable generation
Governance workflows for semantic consistency
Builder Routes
Everyone's deploying AI, but most enterprise systems are reasoning over raw data with no shared understanding of what that data means. Ontologies fix that — and Palantir built an entire $50B platform on this idea. Here's what you need to know.
Most enterprise AI systems fail in production not because the models are wrong, but because nobody defined what 'customer', 'transaction', or 'risk' means consistently across systems. This is a practical implementation guide for building the semantic layer that makes AI grounded, governed, and production-ready.
Google's BigQuery + Gemini NL2SQL pipeline reveals the core problem every team hits: LLMs generate syntactically valid SQL that is semantically wrong. The fix isn't a better prompt — it's an ontology layer that maps business language to your actual schema. Here's the full architecture, the failure modes, and what to build.
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