No-code semantic models, drafted by AI

Semantic modeling is the highest-leverage work in analytics — and historically some of the slowest. The new Semantic Model automation task collapses it from weeks to minutes.

How it works

1. Point the task at your table schemas.
2. Tell it, in plain language, who will use the data and what business questions they need answered.
3. The AI drafts a complete semantic model: entities, relationships, joins, and business definitions.
4. Review it on a visual entity canvas — drag, edit, refine, approve.

Why it matters

• It runs inside the automation framework, so models can be scheduled, versioned, and re-run like any other job
• Table aliasing and data-warehouse semantic support landed in the same release window
• The output feeds everything downstream: it's the gold layer Databasin One reasons over and the foundation for curated views across the platform

The questions you ask shape the model you get. That's the inversion that matters: instead of modeling the data and hoping it answers the business, you start from the business and model backwards.