Topic Hub
Connect business language to data reality using semantic models that reduce hallucinations in analytics and agents.
Business concept to schema mapping
Semantic retrieval before generation
Governance and maintenance for evolving schemas
Builder Routes
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.
Hey there! Ready to dive into Improving Question Answer Semantics With Hyde? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
