Invoice OCR and document automation

Problem

Accounts payable teams spend 3–8 minutes manually keying each invoice into their ERP. At 200 invoices a month that is over 25 hours of error-prone data entry. One transposition mistake on a total or due date can cascade into payment disputes and reconciliation work that takes far longer to unwind. The system ingests invoice PDFs from any source: email attachment, scanned upload, or API push, and returns validated structured JSON in under 3 seconds. No templates. No per-vendor configuration. Works on scanned images, digital PDFs, and mixed-quality documents.

System demo

Architecture

MetricsBenchmarked on SROIE public dataset. Results reflect prototype performance — production accuracy improves with domain-specific fine-tuning.

Tech stack

Production considerations

• Document variability — Invoices have no standard layout. The pipeline handles multi-column, multi-page, and rotated documents without per-vendor templates.
• Validation logic — Extracted line item subtotals are cross-checked against the stated total. Mismatches are flagged for human review rather than silently passed downstream.
• Scanned document quality — Low-resolution or skewed scans degrade OCR accuracy. The pipeline applies deskewing and contrast normalisation before OCR to recover legibility.
• Data residency — Invoice data contains supplier details and financial figures. The pipeline can run fully on-premises with a local LLM to satisfy data residency requirements.
• ERP integration — Output JSON schema is configurable per client. Native connectors available for QuickBooks, Xero, and SAP via webhook or direct API.