See a prompt go through the layer.
Pick a case. Follow one sentence from the keyboard to the model and back. The names below are made up.
Summarize the settlement offer from Marek Zieliński to Nowak & Partners in case II C 418/25.
Summarize the settlement offer from [PERSON_001] to [ORG_001] in case [ID_001].
Summary: [PERSON_001] offers to settle with [ORG_001] in case [ID_001]. Next step: confirm the payment terms before the hearing.
Summary: Marek Zieliński offers to settle with Nowak & Partners in case II C 418/25. Next step: confirm the payment terms before the hearing.
Illustrative example. Names, amounts and IDs are invented.
Between your app and the model.
Where people type prompts and read answers.
Swaps sensitive data for tokens on the way out, and back again on the way in.
OpenAI, Anthropic, Google, Mistral or any LLM endpoint.
Five jobs, done every time.
- 01
Find
Spots names, emails, dates, amounts and IDs in a prompt before it goes out.
- 02
Replace
Swaps each one for an opaque token. The model gets the question, not the people in it.
- 03
Restore
Puts the real values back into the answer, so your team reads a complete reply.
- 04
Apply your rules
Custom entity types, domain rules and role-based access. Add project codes, product names or client IDs that generic detectors miss.
- 05
Keep a record
An audit trail of what was processed, when and under which policy, with retention you configure.
Try it on your own prompts.
Start with the Individual plan today, or book a walkthrough for your team.