Before a fine-tune or training run.
The team wants to catch hidden instructions, duplicate templates, leaked examples, synthetic residue, or source artifacts before the data becomes model behavior.
Training Data Integrity
Review training data before it changes model behavior.
Use this when a team is preparing instruction, conversation, preference, customer-derived, or synthetic examples for a fine-tune or training run and wants reviewable evidence before the run starts.
Rows, clusters, and source context worth reviewing.
Datascreen turns a dataset into a findings queue with evidence attached, so reviewers can decide what to keep, remove, fix, or escalate.
Source: Google Research, Deduplicating Training Data Makes Language Models Better, 2021.
Watch walkthrough
Product demo: review a fine-tuning batch before it reaches the model.
Show an uploaded dataset becoming a review queue, with hidden instructions, repeated templates, leaked examples, and source residue grouped into decisions a data lead can act on.
What the product needs to make the decision obvious.
Issue grouping
Cluster related rows so reviewers can handle patterns instead of treating every row as isolated.
Source evidence
Keep file, field, and source metadata visible beside the row that triggered review.
Reviewer decisions
Turn findings into explicit keep, remove, fix, or escalate outcomes.
Pre-run handoff
Export a record the training owner can review before the job starts.
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.