Context-aware data labeling
Label with context.
Review with confidence.
A local-first workspace for small AI data teams to label text, run a transparent second pass, and create verifiable consistency receipts.
Local-first workspace
Build a reviewable batch.
Label up to 100 short texts, simulate a second-pass review, then create a deterministic consistency receipt—without sending the batch to a model.
Data batch
Text items
Blank lines are ignored. Repeated text stays separate.
Label & review settings
Categories
Add at least one category before labeling.
Review summary
Progress at a glance
Completion
0%
Labeled
0/0
Reviewed
0
Pending
0
Low confidence
0
Disagreements
0
Consistency receipt
SHA-256 evidence digest
The canonical digest will appear here.
This receipt can show that normalized data is unchanged. It cannot prove authorship, truth, rights, reviewer independence, or label quality.
Your work stays in this browser until you export it.
Business proof
Receipt anchoring & history
Save a truthful local proof, or—only when verified contract configuration exists—anchor the current SHA-256 digest through an explicit zero-value contract call.
No network request has been made.
No matching proof records.