The challenge
Data pipelines were breaking silently — bad records slipping through until someone downstream noticed the numbers looked wrong.
Our approach
- 1
Built a rules engine for known data quality checks (nulls, ranges, referential integrity) plus an LLM layer for anomalies rules can't catch.
- 2
Added a self-healing step that proposes corrections for common issues, with human approval before anything is auto-fixed.
- 3
Built a live dashboard so data issues are visible the moment they're detected, not after a downstream report fails.
Capabilities
- Real-time data quality scoring
- AI-assisted anomaly detection
- Self-healing suggestions with human approval
Tech stack
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