Our process

From brief to training-ready dataset.

A clear workflow turns a robotics requirement into a defined real-world data program, structured labels and a quality-reviewed final delivery.

Align on the target behaviour, environment, data types, labelling schema, success criteria and approved project boundaries.

Run repeatable task sessions in suitable environments using the agreed protocols, permissions and operating constraints.

Apply the agreed annotations, metadata and dataset structure to the actions, objects, states and events relevant to the project.

Review outputs against the acceptance criteria, resolve quality issues and document the agreed level of verification.

Organise the final dataset, accompanying metadata and project documentation, then transfer it using the agreed delivery method.

Scope, responsibilities, approvals, timelines and acceptance criteria are established before the project proceeds.

Approved areas, people, identifying details, restricted material, access and retention are considered as part of project design.

Output format, metadata and quality expectations are documented so the final dataset is easier to integrate into the intended workflow.

Send us the task, environment and target data structure and we can discuss fit.