Define Once. Trust Always.
Mitzu's semantic layer lets your team define business metrics once - so every dashboard, report, and analyst always works from the same trusted numbers from your data warehouse.
Agentic semantic layer
AI agent
“Active users last week?”
Semantic layerData warehouse
Snowflake · BigQuery · Databricks
Trusted by leading companies worldwide
Most AI Analytics Tools Are Flying Blind.
Raw Schema Is Meaningless to AI
Raw table and column names rarely encode business meaning. Without a semantic layer, each query is still a guess.
Hallucinated Metrics Destroy Trust
If the AI doesn't know your metric definition, it invents one. The number looks right but drives the wrong decision.
Metric Drift Across Every Tool
Your metrics are already defined in your data warehouse. Generic AI tools skip them and create an ungoverned second layer.
No Transparency, No Accountability
Many tools hide the generated query. If you can't inspect the logic, you can't verify or defend the result.
Get started in minutes, not weeks.
Connect your data warehouse
Create a semantic layer
Ask anything
Connect your data warehouse
Create a semantic layer
Ask anything
Full SQL Transparency. Every Answer. No Black Box.
Every answer includes generated SQL, visible and auditable by default. Analysts can review, verify, or extend queries before sharing insights.
Agentic, Not Just Conversational. The Agent Executes.
Mitzu doesn't just suggest SQL. It picks the right method (funnel, retention, segmentation), runs it on your warehouse, and returns an interactive, governed result.
Proactive Monitoring Grounded in Real Metric Definitions.
Mitzu monitors KPIs against the definitions your team set in dbt. When a KPI shifts, it sends a grounded explanation in Slack or email.
AI Insights
Auto-detected today
Conversion Spike Detected
Mobile conversions up 23% in the last 24 hours
Unusual Drop-off
Checkout abandonment increased on Safari browsers
Growth Opportunity
Users from LinkedIn convert 2x better than average
Why Mitzu is different.
Mitzu vs. General LLMs
ChatGPT, Claude, etc.
General-purpose AI lacks data context and can hallucinate analytics results.
Mitzu vs. Product Analytics
Amplitude, Mixpanel, etc.
Legacy tools require data copying and rigid event schemas.
Mitzu vs. BI Tools
Looker, Tableau, etc.
Traditional BI requires SQL expertise and weeks of setup time.
Works With Every Major Data Warehouse.
Frequently Asked Questions About Mitzu's Agentic Semantic Layer
Stop Hoping Your Analytics Agent Gets It Right.
Give it business context, not guesses. Connect your dbt semantic layer to Mitzu in under 10 minutes.