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Agentic product analytics that tells you whyusers churn

Ask why a metric moved — Mitzu runs a diagnosis directly in your data warehouse and comes back with trusted findings.

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Works with Snowflake, BigQuery, Databricks, Redshift, and ClickHouse. Setup in 10 minutes.

Trusted by leading companies worldwide

Ableton
BrokerChooser
Prezi
Khatabook
Fluenta
Guided eLearning
Raptor
Munch
Suunto
52 Entertainment
Colossyan
Nansen
Shapr3D
Transfr
Physics Wallah
Capabilities

Built for questions that demand deep diagnosis.

Most agentic analytics tools wait for simple questions. Mitzu tackles the hard ones, the kind no single query can answer. It works the problem from multiple directions and returns findings built on product analytics methodology: funnels, retention windows, and cohorts, applied consistently every time.
#analytics
SQL Visibility

AI that data teams actually trust.

Every answer from Mitzu comes with full transparency into the generated SQL. Analysts can verify and approve the logic before sharing insights with stakeholders.

Full SQL visibility - no blackbox AI

See exactly what query is running against your warehouse

Analyst approval workflows

Mark queries as verified before sharing with the team

No hallucinations

AI uses your actual schema and semantic layer definitions

query.sql
Approved by Analyst
1-- Generated by Mitzu AI
2-- Query: "What is our DAU this week vs last week?"
3 
4WITH this_week AS (
5 SELECT
6 COUNT(DISTINCT user_id) AS dau,
7 event_date
8 FROM analytics.events
9 WHERE event_name = 'session_start'
10 AND event_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)
11 GROUP BY event_date
12),
13 
14last_week AS (
15 SELECT
16 COUNT(DISTINCT user_id) AS dau,
17 event_date
18 FROM analytics.events
19 WHERE event_name = 'session_start'
20 AND event_date < DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)
21)
Execution time: 1.2s | Rows scanned: 2.4M
Onboarding

Get started in minutes, not weeks.

From connection to insight in under 10 minutes. No complex setup, no data engineering required.
1
2 min

Connect your data warehouse

Agent config scans your warehouse and creates semantic layer.
2
5 min

Create a semantic layer

No need to write YAML at all. The produced data catalog is designed specifically to work with product analytics.
3
Instant

Ask anything

Our deterministic SQL engine makes sure there are no hallucinations.
Integrations

Works with your modern data stack.

Mitzu natively integrates with whatever is in your data warehouse: BigQuery, Snowflake, Databricks, Firebase, GA4, custom event schemas, legacy tables. No clean dbt project required.
Snowflake
Databricks
BigQuery
Redshift
AWS Athena
Postgres
Trino
Firebolt
ClickHouse
Fabric
Features

Every analysis type. Trusted by design.

The full product analytics toolkit, funnels, retention, segmentation, journeys, cohorts, accessible through natural language. The methodology is built into the engine.

Funnel Analysis

Track conversion rates through multi-step user flows and identify drop-offs.

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Segmentation

Slice and dice your data by any dimension to uncover hidden patterns.

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Retention Cohorts

Measure how well you retain users over time across different segments.

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Journey Mapping

Visualize user paths with Sankey diagrams and discover optimization opportunities.

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Dynamic User Cohorts

Group users by shared characteristics and track performance over their lifecycle.

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B2B Account Analytics

B2B-ready metrics for tracking expansion, churn risk, and engagement at company level.

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Comparison

Built differently from day 1

Most analytics tools fetch whatever you ask. Mitzu hunts.

She's been watching your warehouse since you connected it: every event, every schema, every filter. When you ask why a metric moved, she runs a full investigation and comes back with the reason, grounded in product analytics methodology.

You don't even have to ask why. Mitzu already figured it out.

Mitzu vs. General LLMs

ChatGPT, Claude, etc.

General-purpose AI lacks data context and can hallucinate analytics results.

Feature
Mitzu
Others
Connected to your actual data
No data hallucinations
Real-time warehouse queries
Trusted SQL queries
Analyst approval workflow

Mitzu vs. Product Analytics

Amplitude, Mixpanel, etc.

Legacy tools require data copying and rigid event schemas.

Feature
Mitzu
Others
No data copying required
Works with any data model
Natural language queries
Unlimited ad-hoc analysis
Unlimited events included

Mitzu vs. BI Tools

Looker, Tableau, etc.

Traditional BI requires SQL expertise and weeks of setup time.

Feature
Mitzu
Others
Self-service for all teams
Conversational interface
Setup in minutes
Built-in product analytics
Proactive anomaly alerts
Mitzu combines the best of AI, Product Analytics, and BI — in one platform
Testimonials

What our customers say about us

See what our customers are saying about Mitzu's AI analytics agent.
“The biggest change is the flexibility, our business teams see the same interface, but under the hood, everything is more powerful and easier to maintain.”
Paavo Keisanen
Paavo Keisanen
Data Architect at Suunto
“Effortless Integration, avoids redundant data storage and reduces time to insights at half the cost of standard product analytics tools. All of this is possible because of their amazing Customer Success Team.”
Sakshi Barnwal
Sakshi Barnwal
Head of Data Engineering at Khatabook
“Mitzu didn't just improve our tooling, it transformed our culture around data. We've gone from reactive reporting to proactive exploration.”
Jean-Christophe Lavocat
Jean-Christophe Lavocat
Head of Data & Growth at 52 Entertainment
“Mitzu transformed our data into a reliable, transparent resource, removing the uncertainty of third-party tools. By giving us full control over our data, improving reliability, and offering faster query responses, it became an invaluable tool for our team. I highly recommend Mitzu to any team looking to generate insights for everyone, without the need for a dedicated analytics team.”
Ivan Santini
Ivan Santini
Lead Data Analyst at Prezi
“Before Mitzu, we struggled with high costs and incomplete data. Now, all events are centralized, allowing teams to easily access data, resolve disagreements with charts, and share live data links for clarity.”
Dora Szabo, PhD
Dora Szabo, PhD
Data Lead at Shapr3D
“A godsend for folks who have to keep all the data first party. You've got a lot of in education. There are lots of privacy constraints and it being warehouse native is a huge deal for us.”
Justin Goff
Justin Goff
Senior Product Manager at Transfr
“Mitzu transformed how our marketing team interacts with data. We went from waiting days for reports to getting instant answers. The AI assistant understands our metrics and delivers exactly what we need to optimize campaigns in real-time.”
A
Albert Wettstein
CMO at Munch
“As a technical leader, I appreciate that Mitzu gives us complete data ownership while eliminating the analytics bottleneck. Our engineering team no longer gets flooded with ad-hoc SQL requests - everyone can self-serve with confidence.”
Oliy Barret
Oliy Barret
CTO at Rythm
“The onboarding funnel improved by 30%, and ad hoc reporting is 50% faster, creating quicker, data-driven decisions. Mitzu is our Swiss Army Knife for data. It helps with quick, trustworthy insights without reliance on data teams.”
Tamás Kocsis
Tamás Kocsis
Director of Product Management at Colossyan
FAQ

Frequently asked questions

Got questions? We've got answers. If you can't find what you're looking for, reach out to our team.

The answer to why is in your warehouse. Let’s find it.

Connect in 10 minutes and ask the question your team has been putting off.

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