PDF and invoice data extraction

Problem: manual data entry from PDF invoices (suppliers, amounts, dates, line items) occupied 2 FTEs full-time with a 5% error rate. Solution: automatic extraction pipeline (OCR + Mistral for structured understanding) deployed sovereignly, with human validation for ambiguous cases. Result: 90% of invoices processed automatically, error rate reduced to 0.3%, 1.5 FTE saved.

Problem

manual data entry from PDF invoices (suppliers, amounts, dates, line items) occupied 2 FTEs full-time with a 5% error rate.

Solution delivered

automatic extraction pipeline (OCR + Mistral for structured understanding) deployed sovereignly, with human validation for ambiguous cases.

Result achieved

90% of invoices processed automatically, error rate reduced to 0.3%, 1.5 FTE saved.

Why this matters for SMEs

This case shows how clear scoping, fit-for-purpose tooling and explicit gain tracking turn a business need into an operational result for an SME.

How to evaluate this case

To reuse this approach, check the starting context, delivered scope, maintenance ownership and available measures. A solution creates value through team adoption as much as through technology. This checklist helps compare the problem addressed here with your own situation before starting a prototype or integration.