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Image, video and text annotation — bounding boxes, polygons, segmentation, keypoints and classification — plus dataset cleaning, quality control and human-in-the-loop workflows.
Annotation looks like the simple part of a machine-learning project and is where most of them are quietly ruined. Two annotators shown the same ambiguous photograph will label it differently unless someone has already decided, in writing, what to do with a partially hidden object, a reflection, a blurred edge or a case that falls between two classes.
So the guidelines come first and the labelling second. A specimen set is labelled, the disagreements are found, the guideline is amended to settle them, and only then does volume work begin. It is slower for the first week and considerably cheaper across the project.
Edge cases decided in writing first, so two annotators on the same image reach the same answer.
Inter-annotator agreement is calculated and reported on every project, not claimed in a proposal.
A percentage of each delivery is independently re-checked; a failed sample means the batch is redone.
A held-back, independently labelled set stays with you so our quality claims are checkable.
COCO, YOLO, Pascal VOC, JSON Lines or a custom schema — delivered ready for your pipeline.
Low-confidence production predictions routed for correction and fed back as training data.
Delivered in the format your training pipeline expects — COCO, YOLO, Pascal VOC, JSON Lines or a schema you specify.
Anyone can draw boxes. What you are buying is consistency across tens of thousands of them, and consistency is a process rather than a promise.
Annotation is not always a one-off purchase. Where a model is already in production, the more useful arrangement is a continuous one: low-confidence predictions are routed to a review queue, corrected, and fed back as new training examples. We build that queue as software and can staff the reviewing, or hand it to your team with the tooling.
Teams training their own computer-vision or language models who need labelled data they can audit, delivered in the format their pipeline already expects. You do not need to buy the modelling from us to buy the dataset.
It also suits a business whose model is already in production and now needs a continuous review queue, where low-confidence predictions are corrected and fed back as new training examples.
Straight answers, including the ones that rule us out.
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