Annotation Quality
Multi-layer QA frameworks that guarantee annotation accuracy, inter-annotator consistency, and production-ready data at every stage of your pipeline.
Quality is Not an Afterthought
At CoreLabel, quality assurance is embedded at every stage — not bolted on at the end.
Our QA framework combines inter-annotator agreement checks, senior reviewer sign-off, and automated consistency scans to deliver datasets you can trust in production. Every label is traceable, every batch is auditable.
Inter-Annotator Agreement
Multiple independent annotators label each sample independently. Agreement is measured using Cohen's kappa, with a minimum threshold of ≥ 0.75 required before a batch is approved. For agentic and robotics work we score agreement at the episode level as well as per item — a trajectory can agree on every individual step and still disagree on whether the path as a whole was correct. Disagreements below threshold are escalated to senior reviewers.
Automated Consistency Scans
Rule-based and ML-assisted checks flag statistical outliers, class imbalance, and schema violations before a batch ever leaves our pipeline.
Senior Review & Sign-off
Every batch undergoes a final pass by a domain-experienced reviewer who validates edge cases, ambiguous instances, and guideline adherence.
Full Audit Trail
Each annotation is timestamped and linked to the annotator, reviewer, and guideline version — giving you complete traceability for compliance and model debugging.
Quality Metrics Dashboard
Receive per-batch accuracy scores, Cohen’s kappa coefficients, and defect-rate trends so your ML team can make data-driven decisions on training set composition. Every metric is exportable and auditable.
Continuous Feedback Loops
Model performance signals feed back into labeling guidelines and annotator training, continuously raising the quality floor across projects.
The QA Lifecycle
Ready to Raise the Quality Bar?
Tell us about your project and we will design a QA framework that matches your accuracy requirements.