Document & Identity Consistency-Checking Agent

Manual document verification (for admissions, loan applications, scheme enrollment) is slow and error-prone. A pre-screening agent that flags obvious inconsistencies can save reviewers significant time — as a screening aid, not a final verdict.

Develop an agent that checks a set of uploaded documents for internal consistency (name, date, address matches) and produces a flagged report for human review.

1. OCR extraction of key fields from uploaded documents 2. Text normalization (handles spelling variants, date formats, abbreviations) 3. Cross-document consistency checking 4. Flagged report with confidence score 5. Clear disclaimer that final judgment remains human

An OCR pipeline for structured field extraction A normalization layer to reduce false-positive mismatches A consistency-checking engine across documents A human-readable screening report

OCR: Tesseract / EasyOCR Matching: Fuzzy string matching (e.g., Levenshtein distance) Reasoning/reporting: LLM for report generation Data: Self-created mock/sample documents only — never real personal data

Faster pre-screening of document sets before human review Reduced manual workload for verification teams A reusable consistency-checking pattern for enrollment/verification workflows

Pre-screening documents for scheme enrollment Admissions document verification support Loan application document pre-checks

OCR extraction accuracy Quality of consistency logic (correctly flags real mismatches, doesn't over-flag minor variants) Clarity of the final report