Data quality watchdog
Pipelines and key tables are watched around the clock for missing loads, schema drift, sudden distribution shifts and duplicate explosions. When something breaks, the owner gets a diagnosis — before the bad numbers reach a dashboard or a decision.
What it solves
Silently broken data
Failures surface within minutes, not after the board meeting.
Eroding trust in dashboards
Issues are caught and labeled before anyone sees wrong numbers.
Slow root-cause hunts
Alerts arrive with a diagnosis, not just a red light.
How it works
- 01
Watch
Pipelines, loads and key tables are profiled continuously.
- 02
Detect
Missing data, schema drift, distribution shifts and duplicates trigger checks.
- 03
Diagnose
The agent traces the issue to its source and drafts the explanation.
- 04
Alert
The owner gets the diagnosis and affected dashboards are flagged.
Before & after
Without it
- Broken pipelines discovered by dashboard users
- Wrong numbers make it into decisions
- Root cause takes a day of digging
- Trust in data erodes with every incident
With it
- Failures caught minutes after they happen
- Affected reports flagged before anyone reads them
- Alerts come with a diagnosis and the likely source
- Dashboards people can trust again
Your process could be next.
Tell us what eats your team's time — we'll show you what an AI prototype could do about it.