Security · Legal · IT
Coverage is limited to the configured data rules and deployment profile. Email, Turkish IBAN/identity/phone patterns and organization-defined values become request-scoped placeholders in enforce mode and are restored only through an authenticated mapping. Report mode measures only; it does not block data transfer.
You are not bringing the AI inside your company. You are putting a wall between your company and the AI — the data stays in, the thinking happens out.
Inside your perimeter
Outside your perimeter
"Write a thank-you email to Ahmet Yılmaz for his $45,000 order."
01"Write a thank-you email to [PERSON_1] for his [AMOUNT_1] order."
02"Dear Ahmet Yılmaz, thank you for your $45,000 order…"
03The returning text is compared against your company data, so what is verified and what is not are separated on the same screen.
No complex menus or manual forms. Just tell the assistant what you need; it drafts, you approve.
"No customer names or contract amounts go to any AI."
"Show me which sensitive data tried to leave this month."
"Legal can only use approved models."
Customer names, identity numbers, contract amounts — whatever you set a rule for stays inside your perimeter, and only the placeholder version goes out. The real values are restored when the answer returns. Nothing is missing for your people, and nothing is visible to the provider.
All AI traffic passes through the wall. Which team, with which data, may use which model — you write the rule once and every application inherits it. Who asked what and what it cost sits on one board.
Today the control focuses on the data-egress boundary. Model narrative still requires human review until grounded citations and measured claim verification are complete.
No. Your team keeps working with the tools they know; the checks run in the background in well under a second. The goal is not to block AI use but to make it safe.
It does not. Sensitive information is not deleted — it is replaced by a placeholder that preserves its type. The AI writes knowing there is a person, an amount or a date there, and the real values are restored on the way back to you.
The wall sits in front of your tools rather than replacing them. The models your team uses keep working behind it, and you decide which ones stay open and which job goes to which model.
Because sensitive data never leaves your perimeter in identified form, the cross-border transfer question starts from a different place. Every request — who, when, which data type, which model — is logged without the sensitive content, so the trail an auditor asks for is already there.
We do not currently promise complete automatic hallucination detection. Structured queries use deterministic controls, while narrative answers still require human review. Grounded RAG and measured claim verification are in development.
All assistants share the same foundation and can be activated anytime.
In a 15-minute live demo, we'll build this assistant tailored to your business processes.