Use cases

Choose the AI data workflow you need to review.

Each use case maps to a product workflow: choose the problem, point Datascreen at the source, review the findings, inspect evidence, and export a record.

Training Data Integrity

Review training data before it changes model behavior.

Eval Set Leakage

Check whether evaluation data can still be trusted before results are reported.

External Data Integrity

Inspect public, vendor, or third-party datasets before they enter AI workflows.

Data Poisoning

Surface adversarially useful rows and trigger patterns before they reach training data.

Dataset Changes

See what changed before retraining, re-evaluating, or appending new data.

Synthetic Data Risk

Review synthetic-heavy data before recursive patterns and low-diversity rows accumulate.