Building AI-Ready Semantic Foundations for Enterprise Analytics
Across retail, insurance, telco, and financial services, teams are building their own chat-with-data agents, each with its own definitions of metrics and business context. Nothing carries over from one agent to the next, so business users and leaders get conflicting answers. In longer sessions, agents also drift and invent their own definitions because nothing keeps them inside guardrails.
In this session, Precisely shows how a governed semantic model addresses both problems. Using a telco churn example, an AI copilot drafts a semantic model from certified definitions, relationships, and business terms instead of inventing them. The model is tested with live queries against Snowflake and certified by a data steward, with business domain owners certifying any new definitions. The certified model is then exported and imported into Snowflake Cortex without being rebuilt, so one model can be reused across data systems.
You’ll learn:
- How certified definitions, canonical tables, and verified queries give every agent the same answer
- How a semantic model acts as a guardrail that keeps agents inside governed data
- How data stewards and business domain owners work together to certify a model
- How to build a semantic model once and reuse it across your data systems
