Mobile capture, Aadhaar and PAN extraction, PII masking and an audit-grade archive, end to end with React Native and a Python backend.
Read the guide →Tampered scans and spoofed IDs should never reach your backend. How on-device checks stop them at the moment of capture.
Read the deep-dive →How an NBFC shrank a five-year statement archive without losing a single searchable character, and cut its storage bill with it.
Read the case study →Mobile capture, Aadhaar and PAN extraction, PII masking and an audit-grade archive, end to end with React Native and a Python backend.
Call the Extract Pro API, read back key-value pairs with confidence scores, and handle low-confidence fields gracefully.
Tampered scans and spoofed IDs should never reach your backend. How on-device checks stop them at the moment of capture.
How an NBFC shrank a five-year statement archive without losing a single searchable character, and cut its storage bill with it.
PII handling, consent, retention and breach reporting, mapped to the parts of a document pipeline they actually touch.
When confidence scores and field-level guardrails pay for themselves, with a simple cost model you can adapt.
Where each approach wins, and how to mix them so nothing sensitive leaves the device unless it truly has to.
Deterministic redaction versus format-preserving masking, and how to keep documents useful after you hide the sensitive parts.
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