Real data in.
Zero leakage out.

Catch the fraud, not the customer.

The AI scores every transaction and flags the bad one — but the vendor never sees a real card number, because the number is swapped for a stand-in before it reaches the model.

See the network, never the names.

The AI still exposes the whole mule ring — because the same person keeps the same fake ID across every account, so the shape survives even though no real identity does.

Every scrap of evidence. None of the exposure.

Transactions, merchant notes and call transcripts get reasoned together for a faster ruling — while the raw account details stay inside your walls, because only protected context leaves.

Let AI read the alerts before a human burns a day on them.

The model ranks thousands of alerts so investigators open only the real ones — and it does this blind to identity, because names are restored only after a hit is confirmed, by your own team.

Draft the narrative. Keep the evidence.

The AI writes the suspicious-activity report from the case — but every draft arrives regulator-ready, because Maya logs exactly what was hidden and what was restored, as it happens.

Screen the customer without surrendering the customer.

Screening logic runs and clears the match — while the full record never leaves your environment, because the check happens against protected fields, not the raw file.

The AI reads the passport’s layout — never the person’s name.

Onboarding automation pulls every field it needs from PAN, Aadhaar or utility bills — but the identity is gone before the AI opens the file, because it’s stripped at the pixel level before OCR even runs.

Score the thin file. Protect the person.

The model reasons over bank statements, GST and payslips to approve an MSME borrower — while the applicant’s identity never enters the pipeline, because identifiers are removed before scoring begins.

Answer “why did the AI decline me?” — by default.

Every automated decision comes with a trace a regulator will accept — because Maya keeps a record of exactly what the model saw and what was masked, so DPDP and RBI explainability isn’t a scramble later.

Answer from real history — in seconds, in any language.

The assistant draws on your actual solved-ticket history, not generic scripts — yet no customer PII ever reaches the external model, because it’s masked on the way in and restored only for a live escalation.

The right nudge, to the right account, compliantly.

AI ranks who to contact and how, using real repayment history — but the agent is the only one who ever sees the real identity, and only for the account they’re working.

Triage the claim without exposing the claimant.

Claim forms, bills and images get assessed at speed — while the claimant’s details stay under the insurer’s control, because they’re redacted before the AI ever assesses them. Proven live in insurance.

Your best advisor’s instinct — for every client, twice as fast.

AI reasons over a client’s full financial picture to surface the advice — but a human approves every recommendation and no identifier is exposed, because figures are protected before analysis and a person signs off after.

Read the whole book at month-end — without one real account leaving.

Twelve source systems and billions in revenue get consolidated into AI-ready insight — while every record stays inside the boundary, because data is made analysis-safe before it reaches Power BI or the model.

Train on the patterns — never on the people.

Your fraud and credit models learn from data that behaves exactly like production — but not one real customer is inside the training set, because the links between records are kept while the identities are replaced.

Make SAP and Oracle finally tell the same story.

QA teams test across core, cards and CRM on realistic data — and it actually holds together, because the same customer becomes the same stand-in in every system — true cross-system referential integrity.

Let outside vendors build — on data they can’t misuse.

Approved fintechs test analytics, loyalty and personalisation on real-behaving data — while you hold proof they never touched raw PII, because they only ever receive the protected version.