Ace Intelligence

Vendor Invoice Parser

Vision models that extract structured data from PDF invoices and vendor documents, automatically populating your ERP.

Eliminates manual data entry for finance teams by turning incoming PDF invoices into structured database records automatically.

About the Project

The Vendor Invoice Parser uses vision language models to extract structured data from PDF invoices, receipts, and vendor documents. Fields like invoice number, date, line items, totals, and vendor details are parsed and fed directly into the ERP or accounting system, no manual typing required.

Situation

The finance team at a mid market company was manually entering data from 200 to 300 PDF invoices per week into their ERP system.
Data entry errors occurred in approximately 8% of invoices, leading to reconciliation issues and delayed payments.
The process consumed roughly 25 hours of staff time weekly.

Task

Develop an automated invoice parsing solution that:
extracts 15+ fields from diverse invoice formats
achieves 95%+ extraction accuracy
integrates with the existing ERP API, and
handles edge cases like handwritten notes and rotated PDFs

Action

We deployed a pipeline combining OCR preprocessing (for scanned PDFs) with a vision language model for field extraction.
Invoices arriving in the inbox are automatically fetched, processed, and validated.
Extracted data is reviewed against confidence thresholds, high confidence entries are posted directly to the ERP, while low confidence ones are flagged for quick human review via a simple dashboard.

Result

Invoice processing time dropped from 25 hours per week to under 3 hours.
Extraction accuracy reached 96.5%, reducing error related reconciliation by 90%.
The system processes invoices 24/7, and the finance team now focuses on exceptions rather than data entry.
Payables cycle time improved by 60%.

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