Document Trust
Engine

Manual document review took an average of six minutes per applicant and still missed a meaningful share of doctored or template-generated fake IDs, across dozens of formats and countries — while needing to run fast enough not to lose an impatient applicant mid-signup.

A classifier identifies the document type on sight, OCR extracts every field, and a verification model checks it against the forgery patterns real fraud attempts actually use — font mismatches, tampered holograms, inconsistent microprint.

• OCR First — every field is machine-read before any AI judgment is made — verification starts from extracted text, not a guess at a scanned image.
• AI Validation — trained specifically on real forgery patterns — font mismatches, tampered holograms, inconsistent microprint — not generic image classification.
• Confidence Scoring — every decision carries a number, so an edge case gets flagged for a human instead of silently auto-approved or auto-rejected.
• Structured JSON — every extracted field ties to a name and a value, so downstream systems consume it directly instead of re-parsing a document.
• Modular Processing — upload, OCR, verification, and classification are separate stages, so updating one document format's pattern never touches the others.