De-identification for AI training data early access
One person, one synthetic identity across every Slack message, ticket and email. A written determination the buyer's counsel can read. The mapping stays with you.
Hosted or self-hosted · zero data retention · the mapping stays in your environment
Drop files in any format. Slack, Jira and Notion exports included.
Every name, email, phone and ID is replaced the same way in every file.
Fix a miss once and it applies to every file. The engine also flags anything the judge wants a human to decide.
How we redact
Every document runs the same sequence. Each layer catches what the one before it cannot describe.
Structured identifiers never reach a model. Social security and card numbers, IBANs, API keys, IP addresses, phones and emails are matched deterministically, first, every time.
A NER model reads context and finds the names, companies and addresses no pattern can describe. It runs on our infrastructure, never a third party's.
Priya NairPERSON signed with Acme PressORG at 44 Fenwick RowADDRESS.
Only the ambiguous hits go out, to a zero-retention endpoint. It votes keep, redact or review. It never rewrites your document.
“Dutch”
signature line, offer_letter.docx
Nothing is stored once the run ends. Self-host it and the mapping stays in your environment. What leaves with you is the redacted set and the determination.
Several reviewers work the same corpus at once. A correction made once applies to every file, and the engine recalibrates on the next run.
Every result goes back through the engine before you see it. Every corpus ships with a written determination: methods, recall on your sample, residual risk.
Drop a file and we will take you to pricing to pick a plan. Nothing you drop here is read, sent or stored. Hosted runs are deleted when they finish, or run Redactory on your own infrastructure.
or browse for files
| Original | Replacement | Type |
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Start with 500 documents. Nothing is retained, the mapping stays with you, and the determination goes in the data room.