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Use cases
Use cases
Query your dataAsk grounded questions across your warehouse and docs.Launch intelligenceTrack launches from Slack, Linear, and product data.Reproducible AI analysisKeep SQL, Python, and decisions traceable.Marketing analyticsTurn campaign activity into reports and workflows.Desktop vs. cloudChoose where agents run for your team and data.
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AI analysis you can trust and edit

Slateo's agent produces data flows of SQL queries and Python code that run imperatively against your warehouse. Every step is editable, every result is reproducible, and every query is validated by our semantic SQL engine.

Backed by

Nat Friedman
Daniel Gross
Nat Friedman & Daniel Gross
Shine Capital logo
Twine Ventures logo
Guillermo Rauch
Guillermo Rauch
Christina Cordova
Christina Cordova
Adrien Treuille
Adrien Treuille
Reproducible

A data flow you can see, run, and trust

Our agent produces a directed acyclic graph (DAG) of SQL queries and Python code that runs imperatively against your data warehouse. Every node is a real query or script. Every edge is a real dependency. Every result is reproducible.

  • •Run the whole flow or execute individual nodes
  • •Edit any query with full semantic validation
  • •See dependencies between every analysis step
  • •Add new nodes to extend the analysis on the fly
Analysis Flow
Failure Analysis
Succeeded
Error Types
Succeeded
Daily Run Stats
Succeeded
Org Failure Rates
Succeeded
Classify Errors
Succeeded
Summary Stats
Succeeded
Failure Rate %
Trend Analysis
Succeeded
Error Breakdown
Succeeded
Total Failed Runs
Avg Failure Rate
Analysis Flow
Failure Analysis
Succeeded
Error Types
Succeeded
Classify Errors
Succeeded
Summary Stats
Succeeded
Failure Rate %
Total Failed Runs
Semantic SQL

The world's best intellisense for SQL

Our semantic SQL language server understands your data model, not just syntax. Get real-time type checking, column validation, and quick fixes that know the difference between a date and a timestamp.

  • •Type checking catches mismatches before you run
  • •Column autocomplete from your actual schema
  • •Quick fixes suggest the right cast or function
  • •Semantic validation across joins, CTEs, and subqueries
1-- Monthly active users with session data
2SELECT
3u.user_id,
4u.name,
5COUNT(s.session_id) as total_sessions,
6MAX(s.login_date) as last_active
7FROM dim.users u
8LEFT JOIN fact.sessions s
9ON s.platform_user_id = u.user_id
10WHERE u.created_at > '2024-01-01'
11GROUP BY 1, 2
12ORDER BY total_sessions DESC
Join on platform_user_id to user_id is not a semantic match for fact.sessions. sql-language-server
Type mismatch: date vs timestamp. sql-language-server
142 rows
user_id⋮
name⋮
total_sessions⋮
last_active⋮
u_3891Sarah Chen472025-02-18
u_1204James Park342025-02-17
u_7722Mia Torres292025-02-19
u_0458Liam Nguyen222025-02-15
u_6135Ava Patel182025-02-20

Stop guessing if the AI got it right.
Start editing with confidence.

Reproducible data flows with semantic validation, so every insight is trustworthy and every query is yours to refine.

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