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
Google's BigQuery + Gemini text-to-SQL guidance exposes the core problem every team hits: LLMs generate syntactically valid SQL that is semantically wrong. The fix is not a bigger prompt — it is a governed semantic layer that maps business language to schema, metrics, joins, filters, and entitlements.
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!
A hands-on tutorial on the REA (Resources, Events, Agents) framework applied to banking ontology — from McCarthy's 1982 origins to building a working OWL ontology with Python, RDFLib, SPARQL queries, and AI/ML integration patterns.
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.
Bhanu Pratap Singh