How I built a governed semantic model that eliminates silent analytics failures. The AI interprets intent, the engine generates SQL, and the model is the product.

A complete walkthrough of how a semantic layer is structured: entity modeling, relationship resolution, multi-role dimensions, aggregate context switching, derived metrics, and the definitions that make business data unambiguous.

A self-evolving, self-correcting semantic layer that updates itself as the warehouse changes — and knows when to step back and ask a human.

Most semantic layers make you re-model your warehouse in their own idiom, and you pay for it in marts, derived tables, and drift. The alternative: declare what is already there and let the query generator handle fan-out, SCD2, and allocation at runtime.