Agentic Semantic Layer

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.

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The Problem

Most AI Analytics Tools Are Flying Blind.

The core failure of generic text-to-SQL is missing business context. Raw schema can't tell an AI what your team means by "active user" or "conversion," so it confidently guesses.

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.

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.
Transparent SQL

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.

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
#analytics
Agentic analytics

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

Proactive Monitoring Grounded in Real Metric Definitions.

Mitzu monitors KPIs against the definitions your team set in the semantic layer. When a KPI shifts, it sends a grounded explanation in Slack or email.

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. Text-to-SQL / BI Agents

Cube, Tableau AI, Looker Agent, Veezoo, Querio

They ask the LLM to write SQL against a hand-built semantic layer. Weeks of setup, then a confident guess at the methodology. Obedient. Wrong on the diagnosis more often than you'd like. Mitzu finds the methodology errors before your stakeholders do.

FeatureMitzuOthers
Product-analytics-shaped semantic layer
Zero YAML setup
LLM never writes SQL
Diagnostic investigation
Correct by construction

Mitzu vs. Amplitude, Mixpanel, PostHog

Amplitude, Mixpanel, PostHog

Every event costs money to store. Their AI can only see what's inside their walls. Mitzu goes where your event, billing, CRM and support data already lives and she doesn't charge for every little mouse.

FeatureMitzuOthers
No data copying
No per-event pricing
Joins warehouse-native data
Natural language queries
Unlimited events

Mitzu vs. General LLMs

ChatGPT, Claude, Gemini, etc.

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

FeatureMitzuOthers
Connected to your actual data
No data hallucinations
Real-time warehouse queries
Trusted SQL queries
Analyst approval workflow
Mitzu combines the best of AI, Product Analytics, and BI — in one platform
Integrations

Works With Every Major Data Warehouse.

Mitzu queries your warehouse directly, so data stays put and your semantic layer stays the source of truth.
Snowflake
Databricks
BigQuery
Redshift
AWS Athena
Postgres
Trino
Firebolt
ClickHouse
Fabric
FAQ

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 semantic layer to Mitzu in under 10 minutes.