LLMs Meet Data Warehouses: Building Reliable AI Agents for Business Analytics

Large language models excel at natural language understanding, but struggle with factual accuracy—especially when aggregating business data. This talk explores the architectural patterns needed to make LLMs work effectively alongside analytics databases. I'll cover three key areas: - the integration layer between LLMs and database engines - how semantic models keep AI agents accurate and on-track - and the hypertenancy architecture we've built with DuckDB that makes LLM-data warehouse interactions more efficient, secure, and scalable.