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Case study

Document Intelligence POC in 48h

How we built and deployed an OCR extraction + automatic classification agent for a Paris fintech.

90%
Time saved
95%
Errors eliminated
<30s
Processing time
48h
Deployment time

The context

A Paris-based fintech was manually processing hundreds of invoices per week. Their team spent 20+ hours extracting data (supplier, amount, date, VAT) and entering it into their ERP. Data entry errors caused recurring accounting discrepancies.

Before

  • 20+ hours/week of manual data entry
  • Recurring errors (5-8%)
  • Processing delay: 24-48h
  • Team frustrated by repetitive tasks

After (48h later)

  • 2 hours/week (review only)
  • Error rate < 1%
  • Real-time processing (<30 sec)
  • Team focused on high-value tasks

The process

1

Day 1: Diagnostic + Architecture

30-min call to understand the exact workflow. Analysis of 10 sample invoices. Architecture definition: OCR (extraction) → LLM (classification) → API (ERP).

2

Day 2: Build + Deploy

Agent built with LangChain + GPT-4o. OCR integration for text extraction. Automatic classification pipeline (supplier, amount, date, accounting category). Production deployment with monitoring dashboard.

Tech stack

LangChainGPT-4oOCR (Tesseract)Next.js APIPythonPostgreSQL

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Case Studies — AI Agents in Production | Godi.AI