Before outside data becomes internal data.
The team did not create every row, does not fully control upstream documentation, and wants to sample, scan, and review integrity issues before using the dataset.
External Data Integrity
Inspect public, vendor, or third-party datasets before they enter AI workflows.
Use this when a team pulls a public dataset hub file, vendor export, storage drop, benchmark refresh, or scraped corpus and needs to know what is actually inside before trusting it.
Integrity issues in the imported dataset.
Datascreen surfaces malformed rows, duplicates, missing fields, source ambiguity, low-value examples, and suspicious instructions with enough context to review the import.
Source: The Data Provenance Initiative, Large Scale Audit of Dataset Licensing & Attribution in AI, 2023.
Watch walkthrough
Product demo: pull a real public dataset and inspect the sample.
Show a public dataset import, sample the rows, and walk through malformed examples, duplicates, source ambiguity, and rows that should not be trusted without review.
What the product needs to make the decision obvious.
Import sampling
Review real rows from public, vendor, or third-party sources without pretending the source is clean.
Schema checks
Catch empty fields, malformed records, and inconsistent message shapes early.
Provenance context
Keep source URL, file, and row metadata available during review.
Trust boundary
Make the decision record clear before external data enters the pipeline.
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.