Self-healing data infrastructure
When data breaks,Heal fixes it.
Heal's agents pick up the failed run, trace the blast radius through your lineage, and return a fix with the evidence behind it, before your team opens a single tab.
- Designed for privacy-conscious teams
- Context stays scoped to the incident
- extract
- transform
- validate
- load
- publish
- ✓ Airflow task exception captured+0.2s
- ✓ Lineage impact resolved+0.8s
- ✓ Schema history compared+1.4s
order_total changed from NUMBER to VARCHAR in raw.shopify_orders.
→ fix applied CAST(order_total AS NUMBER)
Context across the tools you already run
The investigation layer
From alert to fix, in one continuous thread.
Heal gathers the evidence automatically, reasons over it, and hands your team a resolution they can verify.
Capture the failure
Heal's orchestration integration captures the failed run, the exception, task state, and the correlated execution metadata around it.
Airflow SDK → Heal SDK
Build the context
Heal combines runtime evidence with warehouse metadata, lineage, telemetry, and context from the tools already connected to the run.
Evidence, connected
Resolve and heal
Agents reason over a scoped incident bundle, then propose or apply the fix, with the evidence trail attached, so an engineer can verify before it ships.
Cause → evidence → action
Better context. Better answers.
Your agent is only as good as what it can see.
A generic copilot sees an error message. Heal sees the execution that produced it, the data it touched, and the systems around it.
Connectors
What happened in your data tools?
OpenLineage
What data is connected and impacted?
OpenTelemetry
What execution caused what?
Warehouse metadata
What changed in the tables underneath?
Heal instrumentation
What did this run know at the moment it failed?
What Heal assembles
Four signals, one incident bundle.
Each source answers a different question about the failure. Together they are enough to act on.
Runtime execution
Task state, retries, exit codes
Data lineage
Upstream cause, downstream blast radius
Warehouse metadata
Schema, types, freshness, row counts
Logs & telemetry
Spans, exceptions, timing
Connected surface
Start with the orchestrator and warehouse you already run.
Heal reads run metadata and schema shape. Add context sources as your needs grow. Tell us which one you need next.
Airflow
Orchestrator
Snowflake
Warehouse
dbt
Transformation
OpenLineage
Lineage
OpenTelemetry
Telemetry
Dagster
Orchestrator
BigQuery
Warehouse
Databricks
Lakehouse
Postgres
Database
Built with intent
Less noise. More certainty.
Evidence before answers
Every conclusion is tied to the signals behind it, so engineers can verify before acting.
Scoped by design
Heal collects incident-relevant context instead of becoming another uncontrolled copy of your data.
Fits the stack you have
Start with your orchestrator and warehouse, then add context sources as your needs grow.
Before you ask
Questions data teams open with.
Does Heal apply fixes on its own?
You choose. Heal can stop at a proposed fix with its evidence attached, or apply it and open the trail for review. Auto-apply is opt-in per pipeline, never a default.
What does Heal need access to?
Read access to your orchestrator's run metadata and your warehouse's information schema. Heal reads the shape of your data: schemas, types, row counts, lineage. Not the rows themselves.
Does our data leave our environment?
Only the incident bundle does, and only what the investigation needs. Context is scoped to the failed run rather than mirrored wholesale into another system.
How long does setup take?
Install the SDK in your Airflow deployment and connect your warehouse. Heal starts building context on the next failed run.
Which orchestrators are supported?
Airflow today. Dagster is next. If you run something else, tell us what. Early access is where we decide the order.
Early access
Spend less time reconstructing failures.
We're working with early data teams to shape Heal around real production incidents.
Join the early access list