Clear requirements. Controlled collection. Verifiable quality.
We turn a robotics data brief into an agreed protocol, a representative pilot and traceable production batches. Every release is reviewed against written technical, task, metadata and sourcing requirements.
From brief to accepted delivery.
A compact five-stage process keeps collection, QA and customer review aligned without making the project unnecessarily complex.
Define the target behavior
Confirm tasks, embodiment, camera views, sensors, environments, labels, volume and intended model use.
Lock collection rules
Document equipment, capture settings, instructions, file naming, synchronization and pass/fail rules.
Validate a small batch
Test representative tasks and edge cases, review ingestion compatibility and refine the protocol.
Collect in controlled batches
Track collectors, sites, equipment and protocol versions while production expands.
Review and deliver
Accepted media, metadata, QC results and supporting documents are reconciled before release.
Four areas reviewed on every project.
The exact thresholds are agreed in the statement of work. Checks are adapted to the data type rather than applied as a generic checklist.
Capture integrity
TECHNICALConfirm that required streams are usable and aligned with the agreed capture specification.
Task validity
BEHAVIORVerify that each episode captures the intended task, outcome and relevant interaction states.
Metadata consistency
STRUCTUREEnsure media, episode records, labels and review status connect through stable identifiers.
Rights and source records
PROVENANCEKeep project-level evidence for the collection source, participant permission and permitted use.
Verification matched to the project risk.
Not every collection requires the same identity checks. We agree the necessary controls before onboarding collectors, suppliers or locations, while avoiding unnecessary personal data in the training-data package.
What accompanies an accepted batch.
The documentation package is scaled to the project. Restricted identity records are not mixed into the normal training-data delivery.
