Supporting capability
Give your model data you can trace and evaluate.
We prepare permitted or licensed datasets for training and evaluation, including collection, normalization, deduplication and agreed labeling workflows.
Define the dataset
Agree the intended task, fields, source rights, sample composition and quality rules. Examine a representative sample before committing to large-scale collection. Separate training, validation and test data to reduce leakage.
Prepare and label
Normalize records, detect duplicates, filter unusable samples and preserve source provenance. Define the annotation guide and review disagreements. Automated labels need sampling and verification against the task requirements.
Deliver and maintain
Deliver the agreed file or database format with a data dictionary and quality report. Track versions, exclusions and collection dates so future refreshes can be compared. Address personal data, retention and deletion requirements in the scope.
Where this fits
A usable dataset does not guarantee model performance. Collection rights, labeling effort, class balance and downstream evaluation remain explicit project constraints.
Operations automation: see the wider workflowEvidence and further reading
Define the scope before committing to a build.
Start with a free conversation about your requirements. A well-defined project can go straight to a proposal. If we need to investigate the data, systems or risks first, we quote discovery separately. Implementation, usage fees and support depend on the agreed scope.