Annotation Quality Assurance

Annotation QA is not a final checkbox — it is an operational system. Our quality assurance services combine golden sets, inter-annotator agreement measurement, auditor consensus, and production error mining so datasets stay aligned with model performance targets.

Golden sets and acceptance thresholds

Curated difficult examples with adjudicated labels become the benchmark every batch must pass before export.

IAA and error taxonomy

Agreement tracked by class, capture condition, and annotator cohort — with root-cause tagging that drives guideline improvements.

Continuous validation loops

Re-audit after taxonomy changes, validate auto-label outputs, and refresh datasets when production drift appears.

Reporting for ML and compliance stakeholders

Weekly dashboards, release gate summaries, and audit trails compliance teams can review.

QA as a managed service

Standalone QA on your labels or embedded QA within full annotation programs with shared PM accountability.

Get started

Strengthen QA before your next model release. Share current accuracy gaps and taxonomy — we propose QA tiers, sampling rates, and reporting cadence.