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Agentic AI generation and labelling of images
Agents generate the frames you cannot collect, then label them in the same pass. Rare and edge classes get coverage without commissioning a new capture run.
What you get
Included in every engagement.
- Synthetic frames for rare and edge classes
- Labels written at generation time
- Per-class confidence on every output
- Prompt and seed recorded per frame
- Balance targets across your class distribution
- Feedback loop back into the generator
How it works
Four steps from kickoff to steady state.
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Find the gaps
We measure which classes your dataset under-represents, and by how much, before anything is generated.
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Generate against them
Agents produce frames aimed at those gaps, with the prompt and seed recorded for every one.
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Label on generation
Each frame is labelled as it is created, carrying a confidence score per class.
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Review and feed back
Reviewers sample the output, and their corrections tune the next generation run.
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