A ten-person keying team costs more with every extra invoice, until confidence scoring decides which documents need a human
The Challenge
Companies that process thousands of receipts or invoices every week from field operations and vendors typically rely on a dedicated team keying the required fields into the ERP. In the lead source case that was a ten-person offshore team. Batch turnaround lags by days, which delays the close and the view of cash, and manual entry introduces errors that must be reconciled downstream. Because cost scales linearly with volume, growth means hiring more people.
The documents themselves make simple automation fail. They arrive in inconsistent layouts, currencies and tax treatments, which defeats template-based OCR. The work is repetitive and low-judgment, so it is hard to staff well: turnover is high, quality drifts, and the close depends on a queue that finance does not control. Every document that needs a correction downstream costs more than it did to enter.
Days of lag. Batches are keyed in bulk, so the close and cash visibility wait for the queue to clear.
Errors that need rework. Manual entry creates mistakes that cost more to fix downstream than they cost to enter.
Cost grows with every document. Headcount scales linearly with volume, so growth demands more people.
Layouts templates cannot handle. Inconsistent formats, currencies and tax treatments defeat simple template-based OCR.
The Approach
A vision-and-language pipeline combines OCR and layout parsing with LLM-based field extraction. Natural language processing reads vendor details, invoice numbers, dates, amounts and line items, and schema-aware validation enforces business rules for dates, taxes, GL mappings, currency and line-item reconciliation before anything posts. Confidence-based quality control sends low-confidence documents to a human reviewer while routine ones go straight through, and the threshold is tuned to the organization's tolerance for error: more review during rollout, less as the system proves itself.
The pipeline fits the existing environment through SFTP, a watch folder or an API connector to the current database, so no ERP replacement is required. Workflow automation carries each document from ingestion through validation to posting. Personal data is redacted, data is encrypted in transit and at rest, and every action is logged for audit. Validation rules mirror the finance team's own controls, so what posts automatically would have passed review anyway.
Techniques: OCR and layout parsing · LLM field extraction · Schema-aware validation (dates, tax, GL, currency) · Confidence-based routing to human review · PII redaction and audit logging
AI role
AI reads each document with OCR and layout parsing, extracts and validates fields against business rules, and auto-processes routine items while routing edge cases to human review.
Automation scope: End-to-end capture of high-volume financial source documents into the ERP, from ingestion through validation to posting.
