Structured field extraction for PAN, Aadhaar, and onboarding forms
Pull the fields your verification workflow actually needs from PAN cards, Aadhaar cards, and onboarding forms — instead of a person retyping every applicant's details by hand.
Fields pulled per document type
Each document type is read for the fields that verification actually depends on, rather than a generic block of OCR text — a PAN card returns the PAN number and name, an Aadhaar card returns the Aadhaar number and address, and an onboarding form returns whatever applicant details it collects.
PAN Card
- PAN number
- Name
- Father's name
- Date of birth
Aadhaar Card
- Aadhaar number
- Name
- Date of birth
- Gender
- Address
Onboarding Forms
- Applicant name
- Contact details
- Declared address
- Signature presence
KYC onboarding shouldn't mean manual re-typing
Verifying a new customer, vendor, or employee typically means collecting a PAN card, an Aadhaar card, and an onboarding form, then manually keying the same name, date of birth, and identifiers into an internal system across each document. That re-entry is slow and it's where transcription errors creep in — a misread digit in a PAN number or Aadhaar number can stall a verification later.
Extraction reads each document type and returns the fields your onboarding system needs directly, cutting the manual step without removing your team's own verification judgment.
PAN and Aadhaar in one pipeline
Both document types are read by the same extraction engine, so onboarding doesn't need separate tools per document.
Scanned and photographed copies
KYC documents are frequently uploaded as phone photos or scans, not clean digital files — extraction is built for both.
Review-first output
Extracted fields are meant to be checked against the source document as part of your verification step, not auto-approved.
Faster onboarding, same diligence
Structured fields cut manual re-typing during onboarding — your team still applies its own verification process on top.
For onboarding, HR, and compliance teams
Customer onboarding at an NBFC or fintech, vendor onboarding in procurement, and employee onboarding in HR all run through the same identity-verification step: collect a PAN card, an Aadhaar card, and a form, then key the details into whatever system tracks that verification. When onboarding volume is high — a lending business bringing on hundreds of customers a month, for example — that re-entry step is where the process actually slows down, not the verification judgment itself.
Extraction handles the re-entry step. A submitted PAN, Aadhaar, or onboarding form comes back as structured fields your onboarding system can accept directly, and your team's own verification — checking the document against the applicant, confirming details match across documents — happens on structured data instead of a photo someone has to squint at.
Handle identity documents with care
PAN and Aadhaar documents carry sensitive personal data, so how they're handled matters as much as how they're read. See our Privacy Policy for what happens to a document once it's uploaded, and only submit documents you have the right to share. For the full extraction approach across all document types, including field-level and full-document modes, see the DocumentsAI homepage.
Extracted results are available as JSON, CSV, Markdown, or a reviewable table — pick whichever format matches your onboarding or CRM system. Whichever format you choose, treat the output as a faster first pass through the document, not a final verification decision; a human should still confirm identity details before onboarding is approved.