Stop trusting data you never checked.
Davynci generates quality rules straight from the knowledge graph, detects anomalies with statistics and ML, and traces every one to a root cause — because it already knows the lineage.
Six quality dimensions, scored per column
Rule generation
Rules you didn't have to write.
Other tools make you author checks by hand. Davynci reads the graph's statistics and standards and proposes the rules itself — you review and activate.
- Auto-generated from discovery stats and standards
- Six categories: completeness, validity, consistency, accuracy, timeliness, uniqueness
- Severity raised for regulated and load-bearing columns
Detection
Anomaly detection tuned for real data.
Naive thresholds drown you in false positives on skewed financial data. Davynci routes detection through robust statistics, so alerts mean something.
- Statistical (Z-score, IQR, KS), LSH clustering, and ML
- MAD-routing → far fewer false positives on log-normal data
- Continuous execution on a schedule, at scale
Resolution
Explained and resolved, not just alerted.
Because the brain knows the lineage, an anomaly arrives with its likely cause and its blast radius — the detective work is already done.
- LLM root cause, grounded in lineage
- Incident correlation across five strategies
- Contracts, SLAs, scorecards, point-in-time standing
Under the hood
Everything the data quality agent does.
Auto rule generation
Rules proposed from the graph — you approve and activate.
Six rule categories
Completeness, validity, consistency, accuracy, timeliness, uniqueness.
Statistical + ML detection
Z-score, IQR, KS, LSH clustering — MAD-routed.
Lineage-aware root cause
LLM explanation with the upstream cause attached.
Incident correlation
Five strategies group related anomalies into incidents.
Contracts & scorecards
SLA-based agreements and audit-ready quality scores.
One brain, many modules
Data quality is one module on the semantic layer.
Govern quality across your whole estate — automatically.
See Davynci generate the rules, catch the anomalies, and explain the cause.