Multi-level quality assurance for annotation, transcription and dataset delivery.
Quality is built into every stage — from annotation guidelines and calibration to multi-level review and client feedback.
Documented label taxonomy, attribute rules and edge-case handling agreed with the client before production.
Annotators trained on project-specific guidelines and golden examples before working on live batches.
Pilot batches run against hardest edge cases first; guidelines sharpened from real disagreement.
Reviewers sample each annotator's work for consistency, ambiguity and guideline compliance.
Senior QA performs edge-case review, rejection rework and final validation before delivery.
Client review findings are fed back into guidelines and retraining for continuous improvement.
Applies labels per project guidelines; first-pass quality captured at the source.
Checks consistency, accuracy and guideline compliance on sampled batches.
Edge-case review, rework and final validation against acceptance thresholds.
Statistical sampling of annotated batches and consistency checks across annotators to surface drift early.
Error categories logged per project to guide guideline updates and targeted retraining.
Ambiguous and borderline items routed to senior QA for resolution and documented in guidelines.
Items failing acceptance criteria are returned for rework and re-verified before delivery.
Tell us your quality thresholds and acceptance criteria.