AI agents fail on enterprise data for one reason: they have no business context. They don't know what ARR means to your company, how you define a customer, or which rules govern your data. So they guess, and the answer looks right until it isn't.
Strategy Mosaic fixes this by giving AI a semantic foundation to work from. In this session, you'll watch us build one live and see what changes when an AI agent has governed business context instead of raw schema.
No SQL required. Mosaic Studio lets you define your semantic model visually, so this session is accessible whether you're an analyst, a data engineer, or somewhere in between.
What you'll learn:
What a semantic model actually is: how Mosaic encodes business logic once so every AI agent, dashboard, and downstream tool reads from the same definitions
How to connect an AI agent to Mosaic via MCP: see the before and after, with the same questions answered against raw schema and against a governed model
What consistency looks like in practice: same question, same answer, every time, regardless of which model you're using
What you'll walk away with:
A clear understanding of how Strategy Mosaic gives AI the business context it needs to answer correctly and consistently
Confidence in how to build a semantic model using Mosaic Studio, without writing a line of SQL
A framework for explaining to your team why AI gets the numbers wrong and what it takes to fix it
Speakers

Kaitlin Qreitem
Associate Sales Engineer
Strategy

Johannes Silhan
Principal Sales Engineer
Strategy