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Data labelling for AI model training
Bounding boxes, segmentation, keypoints, and classification against your taxonomy. Models draft the first pass, senior reviewers confirm the edge cases, and every correction trains the next round.
What you get
Included in every engagement.
- Bounding boxes, polygons, and cuboids
- Instance and semantic segmentation
- Keypoints, landmarks, and pose
- Multi-class and hierarchical classification
- Attribute and relationship tagging
- Two-pass review against a gold set
How it works
Four steps from kickoff to steady state.
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Agree the taxonomy
We map your label schema, the edge cases that decide it, and the bar a batch has to clear before it ships.
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Pre-label with models
A first pass runs automatically, so reviewers start from a draft rather than an empty frame.
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Senior review
Reviewers resolve the cases the model got wrong. Every decision is attributed to a person and a model version.
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Retrain and re-run
Corrections feed the next pre-labelling pass, so the automated share of the work grows batch over batch.
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