Building Trustworthy AI Analytics: Compass + Cube in Practice

Everyone's racing to add AI to their analytics stack. The pitch sounds great: business users ask questions in plain English, get instant answers, no more waiting on data teams.

However, without proper guardrails, your LLM generates SQL queries against raw schemas it doesn't understand. It invents metric definitions that sound plausible but don't match how your business actually calculates revenue, churn, or customer lifetime value. Different users get different answers to the same questions. And when someone asks "where did this number come from?", there's no lineage, no audit trail, no way to verify the result.

In this Deep Dive, we'll show you how to fix it. You'll see a live demo of Compass querying Cube's semantic layer to deliver accurate, governed metrics directly in Slack. We'll dig into the technical details: how Cube defines measures with proper business context, what system prompts prevent hallucinations, and how the integration ensures your team gets trustworthy answers, whether they're asking in plain English or building dashboards.

You'll learn:

• Why AI analytics fails without governance (and what that actually means)
• How semantic layers provide the guardrails AI needs to be trustworthy
• Technical implementation: how Compass + Cube work together to prevent hallucinations
• Live demo: governed self-service analytics that data teams can actually trust

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