Data annotation transforms raw content into structured training signals. Our pipeline orchestrates the full lifecycle from task creation through quality assurance to final export. Annotation schemas are defined using a flexible type system that supports classification labels, span annotations, bounding boxes, polygon segmentation, and relational triples.
Human annotators receive work through a task queue that balances throughput with quality. Inter-annotator agreement is monitored in real time, and disagreements are automatically routed to senior reviewers for adjudication. The system maintains per-annotator performance metrics including accuracy, speed, and consistency scores.
Model-assisted labeling accelerates throughput by pre-populating annotations using a fine-tuned model. Annotators review and correct predictions rather than labeling from scratch, typically achieving a three to five times speedup without sacrificing quality.
Export formats include JSONL, CoNLL, COCO, Pascal VOC, and custom schemas. Annotation provenance metadata is embedded in every export, linking each label to the annotator, timestamp, and review status.