AuraQC

Quality that watches itself.

Rules, pattern detection, and flags — with evidence attached to every escalation.

Rules run first

Structural validation and rubric rules catch clear violations with explainable evidence attached.

ML scores the rest

A lightweight classifier scores risk on ambiguous cases and routes them to review. No guessing.

Every flag is ready to act on.

High-confidence flags produce queue-ready items with reason, evidence, and severity level.

Every decision makes the next catch better.

Reviewer decisions feed back into the classifier. Retraining runs monthly when volume supports it.

AuraQCRulesMLEscalationFeedback
Structural

Schema & type checks

Rubric

Rule validation

Patterns

Anomaly detection

ML

Risk scoring

Escalate

Queue & evidence

Truth check

AuraQC components, APIs, and dashboard are implemented. Precision/recall targets and time savings require production validation.

Explainability

Every flag includes a reason and evidence payload. Reviewers decide faster with context.

Coverage

Structural, rubric, and pattern checks run before ML. Obvious issues resolve instantly.

Escalation

High-confidence detections route to adjudication queues with severity, timestamps, and evidence.

QC Dashboard

The queue that never sleeps.

AuraQC — quality monitor (example)Pipeline v2.4
142
Open flags
1,893
Resolved
96%
Precision (example)

Illustrative values for UI demonstration. Production precision/recall requires validation on your data.

QC-2291RuleSchema mismatch in annotation payloadhigh
QC-2290MLLow-confidence labeling on medical imagemedium
QC-2289PatternDuplicate annotation cluster detectedlow
QC-2288RuleMissing required field: confidence_scorehigh
Ingest
Rules
Patterns
ML Score
Escalate

Bring a batch. We'll show you the flags.