Dataset quality is not a single check at the end — it is validated at every stage of the collection pipeline, with criteria defined specifically for your project.
Every incoming sample is checked against the approved collection design before it moves further down the pipeline.
Samples are checked for completeness against the required fields, formats and coverage targets.
Automated and manual checks identify and remove duplicate or near-duplicate samples.
A sample-based or full review of annotations against the project's labeling guidelines.
Metadata fields are validated for accuracy, consistency and completeness across the dataset.
A final review confirms the dataset meets the agreed acceptance criteria before delivery.
A pilot project is the best way to evaluate our quality process before committing to a full-scale collection.