Customers
Teams shipping faster with infrastructure-aware AI
From seed-stage startups to platform teams: see how Metroflow changes the way data work gets done.
TRUSTED BY DATA TEAMS BUILDING ON
Snowflake
dbt
Airflow
Dagster
BigQuery
4×
Faster impact analysis
3d
Onboarding vs 3 weeks
60%
Fewer escalations
12h
Saved per sprint
Fintech · Series B
From Slack archaeology to queryable lineage
A 40-person data team replaced half-day impact analyses with ten-minute Metroflow queries. New hires trace dependencies on day one.
- Impact analysis: 4 hours → 10 minutes
- Onboarding: 3 weeks → 3 days
- Stack: Snowflake + dbt + Airflow
E-commerce · Growth stage
dbt test failures solved by the agent
When a Fivetran resync broke unique_key tests, Metroflow identified 340 duplicate rows and suggested the exact dedupe fix, grounded in manifest.
- Debug time: 2 days → 2 hours
- PR auto-suggested with context
- Stack: BigQuery + dbt + Fivetran
Healthcare · Enterprise
Self-hosted in the VPC in one afternoon
Platform team audited the open-source connectors, deployed via Docker Compose, and had lineage queries running before end of day.
- Compliance: metadata-only crawls
- Full audit trail on connectors
- Stack: Postgres + Dagster + dbt
"We cut impact analysis from half a day to ten minutes."
"The agent read our manifest before answering. That never happened with ChatGPT."
"Open source meant we could audit every line before prod. Huge for healthcare."