Data quality and deduplication

The operating task

Improve training-corpus integrity through duplicate, coverage and quality controls. Specialist contribution is shaped around the quality of the signal and its usefulness to model development.

Softinator's contribution

Identify duplicates, inconsistent labels and coverage gaps that weaken a training release. The commissioned output includes quality-reviewed dataset and issue register.

Acceptance in use

Assess release integrity and meaningful diversity without discarding useful domain variation merely for uniformity. The engagement defines the baseline, task scope and evidence used to assess the resulting contribution.