The analytics agent that doesn't write SQL
Ask funnel, retention, and cohort questions in plain English. Mitzu's agent composes each analysis against a semantic layer built for product analytics — and a deterministic query engine generates the SQL on your warehouse, so the methodology stays correct.
The agent composes the analysis. The engine writes the SQL.
Mitzu's Analytics Agent does not write SQL. It composes analysis specifications — funnels, retention, segmentations — against a semantic layer specialised for product analytics, and a deterministic query engine turns each specification into SQL that runs in your data warehouse. That is why funnel, retention, and cohort methodology stays correct instead of depending on a model getting the SQL right.
The agent interprets the question
It maps your words to real events, properties, and cohorts in the semantic layer that the Configuration Agent built from your warehouse — filters are suggested from sampled values that actually exist, not invented.
It composes an analysis specification
A funnel's steps and conversion window, a retention cohort and its return event, a segmentation's filters and breakdowns. The agent fills in these parameters. It never authors query text.
A deterministic engine generates the SQL
The same specification always produces the same SQL, executed in your warehouse. Funnel, retention, and cohort methodology lives in the engine, so the model cannot get it wrong — and the SQL is shown for your analysts to verify.
The same architecture powers the agent in the app, in Slack, and over MCP, on top of Mitzu's warehouse-native architecture — read the full mechanism reference at docs.mitzu.io: how the agent works.
Get started in minutes, not weeks.
Connect your data warehouse
Create a semantic layer
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Connect your data warehouse
Create a semantic layer
Ask anything
Built for questions that demand deep diagnosis.
Ask questions like you're talking to a colleague
No more complex SQL queries or waiting for data teams. Simply type your question in plain English and get instant, accurate answers with visualizations.
- Plain English queries - no technical knowledge required
- Automatic query optimization for fast results
- Context-aware responses that understand your business
- Follow-up questions for deeper analysis
Mitzu AI Agent
What was our conversion rate last week?
Your conversion rate last week was 4.2%, up 0.5% from the previous week. The highest performing day was Thursday with 5.1%.
Why did it increase on Thursday?
Thursday's increase correlates with your email campaign launch. Users from the campaign had a 6.8% conversion rate.
A dashboard you have to remember to check is worse than one that watches itself
Scheduled agents run your questions on a cadence and email you only when the results meet a condition you set — a metric drop, a threshold crossed, a cohort behaving differently.
- Runs on the schedule you choose
- Alerts only when your condition is met
- Same semantic layer and deterministic engine as every other surface
- Findings arrive with the supporting charts
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
Works with your modern data stack.
Trusted by leading companies
0
Lines of SQL written by the agent
1
Deterministic engine behind every answer
11
Warehouses queried in place
3
Surfaces: app, Slack, and MCP
Frequently asked questions
Go deeper on analytics agents
Guides on how analytics agents work, what separates trustworthy answers from plausible ones, and the reference documentation behind this page.
What is an analytics agent?
A definitional guide: how analytics agents work, the main architectures, and what makes one trustworthy.
Read the guideAnalytics agents vs text-to-SQL and BI chatbots
Why generic SQL generation falls short on funnels, retention, and cohort methodology.
Read the guideWhy an analytics agent needs a semantic layer
How a product-analytics semantic layer prevents hallucinated SQL and keeps answers reviewable.
Read the guideVendor-silo analytics agents vs warehouse-native
Why agents inside Amplitude or Mixpanel can't reach billing, CRM, and support data in your warehouse.
Read the guideDocs: how the Analytics Agent works
The architecture reference — analysis specifications, the deterministic query engine, and what the agent can and cannot do.
Read the guideDocs: warehouse-native architecture
What Mitzu queries, what it never copies, who pays for compute, and the read access it needs.
Read the guideReady to experience AI-powered analytics?
Start asking questions in plain English and get instant insights from your data warehouse. No SQL required.