FOR DATA ENGINEERS

Your AI Pair Programmer for Data Infrastructure

Stop writing boilerplate. Start building systems that scale.

How Much Time Are You Losing?

Calculate your weekly time spent on:

Writing DBT models and tests5-10 hours
Documenting tables and columns3-6 hours
Running quality audits and checks4-8 hours
Answering ad-hoc data questions6-12 hours
Total Time Lost18-36 hours/week

That's 50-90% of your week on repetitive tasks

Core Workflows We Accelerate

Auto-Generate DBT Models with Tests

Describe what you want in natural language. Get production-ready DBT models with appropriate tests, documentation, and best practices baked in.

You:
"Create a customer lifetime value model"
AI Agent:
✓ Generated model with CTEs
✓ Added uniqueness tests
✓ Included documentation

Document Tables as You Query

Every query you write contributes to living documentation. The AI learns from your usage patterns and keeps your catalog up to date automatically.

After running your queries:
✓ Column descriptions inferred
✓ Usage patterns captured
✓ Common joins documented
✓ Business logic preserved

Run Warehouse-Wide Quality Audits

Scan your entire warehouse for quality issues in minutes. Get actionable reports on nulls, duplicates, schema drift, and more.

Automated checks across 1000+ tables:
⚠ 47 tables with high null rates
✗ 12 tables with duplicates
ℹ 23 tables with schema changes
✓ 918 tables passing all checks

Build Transformations from Natural Language

Skip the SQL boilerplate. Describe the transformation you need and get optimized queries that follow your warehouse's best practices.

Request:
"Join users with their last 5 orders"
Result:
SELECT u.*, o.orders
FROM users u
LEFT JOIN LATERAL (...

Real Time Savings

BEFORE

Creating a New DBT Model

  • • Write SQL from scratch (45 min)
  • • Add tests manually (20 min)
  • • Write documentation (15 min)
  • • Debug and refine (30 min)
~2 hours
AFTER

Creating a New DBT Model

  • • Describe what you need (3 min)
  • • Review generated model (5 min)
  • • Make tweaks if needed (7 min)
~15 minutes
BEFORE

Warehouse Quality Audit

  • • Write custom queries (2 hours)
  • • Run across all tables (3 hours)
  • • Compile results manually (1.5 hours)
  • • Create report (1 hour)
7.5 hours
AFTER

Warehouse Quality Audit

  • • Run automated audit (5 min)
  • • Review AI-generated report (5 min)
10 minutes

Fits Into Your Existing Stack

Works seamlessly with the tools you already use

🔄

DBT

Generate models, tests, and docs that work with your existing DBT projects

📊

Airflow / Dagster

Integrate with your orchestration layer for automated workflows

💾

Any Warehouse

Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, and more

"
We went from spending 60% of our time on maintenance to actually building new features. The AI handles all the tedious stuff while we focus on architecture and innovation.
Sarah Chen
Senior Data Engineer, TechCorp

Ready to Reclaim Your Time?

See how AI agents can transform your data engineering workflow