Check the AI's output against the document before anything is accepted.
Automated extraction speeds up verification but must not replace the officer's judgement. Nanonets shows the extracted data beside the original so the human decision stays where it belongs — on a checked result rather than a raw document.
You review AI output against source images before approving any record. This workflow keeps extraction fast while preserving the accuracy standard Registry operations require.
Turn the pilot into an evidence-based decision on whether to scale.
Get the extracted data into the systems Registry actually works in.
Prove the accuracy on one document type before scaling to others.
Open the processed document.
Review the extracted fields beside the original image.
Check any field flagged as low confidence first.
Confirm the values that are correct.
Approve the document once the data is verified.
Note which fields the model reads reliably and which need attention.
You open a processed passport for [hypothetical applicant]. Nanonets shows: - **Passport Number**: [P########] (high confidence) - **Date of Birth**: [##/##/####] (high confidence) - **Expiry Date**: [##/##/###O] (low confidence) You check the low-confidence expiry date first. The original image shows [##/##/####]. The model misread a character. You correct the field to match the image, confirm the other two fields match the image, and approve the document. You note which field types need attention for future review.
Show the extracted fields beside the original document, highlight low-confidence values for checking first, and approve only once every field has been verified against the image.
Pull the key clauses and signatories out of a long document automatically.
Cross-check extracted values against what Registry already holds.