When generated rows need evidence-based review.
The team is mixing real examples with generated data, augmentation output, or vendor-supplied synthetic rows and needs to see which patterns deserve review.
Synthetic Data Risk
Review synthetic-heavy data before recursive patterns and low-diversity rows accumulate.
Group generated patterns, keep source context visible, and decide which synthetic-heavy rows are useful enough to keep before they enter training or eval data.
Synthetic-risk indicators with origin context.
Datascreen groups repeated templates, model-output residue, low-diversity clusters, and missing source context so reviewers can keep useful rows and clean weak ones.
Product motion: group synthetic-risk patterns, review them with source context, and export the decision trail.
Watch walkthrough
Product demo: clean a synthetic-heavy batch without banning useful rows.
Show generated templates, low-diversity clusters, and model-output residue grouped for review, then keep, fix, remove, or escalate the rows with evidence attached.
How Datascreen makes synthetic-heavy data reviewable.
Group repeated patterns
Show generated templates and repeated structures as clusters, not scattered single rows.
Show origin context
Keep real and generated examples visible together so reviewers can judge the balance.
Review by evidence
Let reviewers decide what to keep, fix, remove, or escalate based on the row and its surrounding pattern.
Export the cleaned set
Save a cleaned version and a record of which generated patterns were accepted or removed.
A prioritized list of rows and clusters that deserve human review.
The row, source, neighborhood, and reason shown together.
A record of what reviewers kept, removed, fixed, or escalated.
A workflow-ready handoff that states what was reviewed and what remains uncertain.