Let AI read the numbers. Not the names on them.
Summarise the ledger, draft the client letter and check the rule, with tax identifiers and bank details replaced before anything is sent.
What you have
Payroll query for Ines Duarte, NIF 48023306L, salary paid to ES91 2100 0418 4502 0005 1332, mobile 612 345 678.
What the assistant receives
Payroll query for ⟦PERSON_1⟧, NIF ⟦DNI_NIF_1⟧, salary paid to ⟦IBAN_1⟧, mobile ⟦PHONE_1⟧.
Synthetic. The reply comes back with the tokens in it and the real values are restored on your machine.
- Built for the formats
- Country packs for Spain, the US, the UK, France, Germany, Turkey and Italy recognise national identifiers as identifiers rather than as digits. Spain shipped first, so NIF and NIE are first-class.
- The numbers survive
- What gets masked is who the figures belong to. Amounts stay usable, so a ledger summary comes back structurally correct and the real details are restored on your side.
- Format-valid when you need it
- Pseudonymised mode substitutes a plausible NIF for a real one, so the model treats the text as ordinary. Tokenised mode makes the veil visible, so you can check it before sending.
A practice runs on identifying data: tax identifiers, bank details, payroll figures, home addresses. The files are made of exactly what should never be pasted into a chatbot.
The free tier of the extension needs no key and no account, so trying this on a real ledger costs two minutes.
What detection catches, and what still slips through, is measured and published on the benchmarks page.
What people ask first
Does masking break the numbers the AI needs to reason about?
Are Spanish tax identifiers like NIF and NIE actually detected?
Can my whole team use it, and what does it cost?
See it work on your own data.
Tell us what you work with and we will walk you through it on a call, on your own files. Nothing leaves your machine while we do.
Your address is used to reply to you, and for nothing else.