Most enterprise AI projects fail for the same reason: the AI understands language, but it does not understand the business. Left on their own, LLMs guess column names, misinterpret relationships, and generate inconsistent answers from the same data.
In this demo, see how AtScale connects directly to Claude through Model Context Protocol (MCP) to deliver governed, explainable AI analytics over enterprise data. Learn how AtScale’s universal semantic layer gives AI assistants access to trusted business definitions, governed metrics, and optimized query generation, without exposing raw tables or relying on prompt engineering.
What You’ll Learn
- Why LLMs struggle without governed business context
- How AtScale makes AI analytics deterministic instead of probabilistic
- How semantic models standardize metrics, KPIs, and calculations across AI and BI
- How Claude uses MCP to discover and query governed semantic models
- How AtScale enforces row- and object-level security automatically
- How semantic query optimization reduces expensive raw SQL generation
- Why governed semantic layers are foundational infrastructure for enterprise AI
