AI-assisted, self-initiated demonstration using synthetic records—not a client project. A working Python utility applies agreed whitespace/case rules, removes exact full-row duplicates, and flags uncertain records without guessing.
Verified sample: 12 input records → 11 retained + 1 duplicate; 7 cells normalized; 6 review flags. IDs 0010 and 10 stay distinct. The original file is preserved byte for byte.
Outputs include cleaned data, cell and record logs, a review list, and reconciled counts. Five integrity checks and seven edge-case tests passed. Source and synthetic sample are available on request.
CSV cleanup & reconciliation — a self-initiated Rivetfern demonstration using synthetic data.
Python validates and normalizes invoice/payment exports, quarantines conflicting records, preserves an audit trail and reconciles totals separately by currency. The reproducible fixture has 90 invoice rows and 92 payment rows; outputs retain 80 valid invoices and 84 valid payments, with exceptions kept visible.
Handover: clean CSVs, reconciliation report, rejected-row reasons, source hashes, runnable code and tests. This is a portfolio demo, not paid client work or a claim of client results.
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