Imagine you’re trying to understand why your company’s sales dropped last quarter. You query your database with a simple natural language question: “Why did sales drop last quarter?” The ideal scenario would be that the AI system instantly provides you with a context-rich, insightful answer  —  something that ties together all relevant data points, trends, and market insights. However, the reality is far from ideal.

Current AI methods for querying databases, such as Text2SQL and retrieval-augmented generation (RAG), fall significantly short. These models are limited by their design, either only interpreting natural language as SQL queries or relying on simple lookups that fail to capture the complexity of real-world questions.