Custom action & semantic annotation
Define labels and delivery rules around your model. Choose an automated pipeline, a human labeling team or a hybrid workflow after a sample review.
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FROM RECORDINGS TO RELEASES
Dataset processing & QA
Organize, validate and release datasets with reproducible manifests. Make exclusions, missing signals and transformations visible before training.
Scoped to your project · Request a quotation
No purchase commitment. Availability, scope, lead time and commercial terms are confirmed after requirements review.
THE SPECIFICATIONKnow what
Know what
you are evaluating.
| Checks | Corruption, dropouts, incomplete episodes and inconsistent labels |
|---|---|
| Privacy | Agreed face, screen or identifying-content handling |
| Splits | Task/scene/subject split rules; leakage checks as scoped |
| Delivery | Customer-controlled destination or agreed transfer channel |
| Traceability | Stable IDs, processing version and issue report |
| Acceptance | Customer-approved sample and measurable batch rules |
SCOPE THE HANDOVERWhat to include
What to include
in your project.
- Input inventory and schema validation
- Frame, timestamp and modality integrity checks
- Duplicate and invalid-episode review
- Agreed privacy transformations and review
- Checksums, manifest and release notes
KEEP BUILDING
Full catalogRelated offerings.
Training-format integration
Map episodes into your training schema, document coordinate conventions and test the loader before committing a full dataset conversion.
YOUR NEXT MOVE
Let's build something