Dataset processing & QA
Organize, validate and release datasets with reproducible manifests. Make exclusions, missing signals and transformations visible before training.
Reference image. Exact equipment and configuration are confirmed per project. Image credits ↗
TURN MOTION INTO MEANING
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.
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.
| Delivery mode | Automated pipeline, human labeling or hybrid review |
|---|---|
| Granularity | Task, step or atomic action |
| Labels | Customer-defined actions, objects, locations and outcomes |
| Language | English; additional languages scoped by project |
| QA | Sampled review, ambiguity resolution and agreed acceptance rules |
| Input | Customer-provided or commissioned recordings |
| Output | JSON / CSV / Parquet or customer-defined schema |
SCOPE THE HANDOVERWhat to include
What to include
in your project.
- Customer-defined ontology, granularity and annotation guidelines
- Pilot sample to validate automation and human effort
- Agreed automated, human or hybrid delivery workflow
- Temporal segments and language descriptions
- Consistency checks and adjudication log
- Versioned labels and acceptance report
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