Better labels, better models
Model quality is capped by label quality. We combine clear guidelines, trained annotators and model-assisted pre-labeling with a multi-stage review process, then report agreement scores so you can see exactly how consistent the labels are.
Annotation types
- Text: intent, topic, sentiment, named entities, toxicity, relevance
- Images: classification, bounding boxes, segmentation, keypoints
- Documents: key-value extraction, table extraction, layout tagging
- LLM data: response ranking, preference pairs, instruction datasets
Quality process
Each batch goes through annotation, peer review and expert audit. Disagreements feed back into the guidelines, so quality improves as volume grows.