Automated Invoice Processing Pipeline
AI Automation · 82% reduction in processing time
The system now processes 500+ invoices per week with minimal human intervention, freeing the finance team to focus on analysis rather than data entry.
The challenge
A financial services firm was losing 20+ hours per week to manual invoice data entry and validation. Every invoice required a human operator to read, extract key fields, cross-reference against purchase orders, and manually enter data into their ERP system — a process riddled with errors and delays.
The bottleneck wasn't just speed. Inconsistent data entry led to reconciliation failures downstream, creating cascading issues in financial reporting. The team had tried template-based OCR solutions before, but the variety of invoice formats from 200+ suppliers made rule-based approaches unworkable at scale.
Our approach
Deployed a document intelligence pipeline using LangChain to orchestrate multi-step extraction: OCR → field identification → validation → ERP insertion. The system handles 15+ invoice formats automatically.
Built a confidence-scoring mechanism that routes low-confidence extractions to a human-in-the-loop review queue. Each human correction feeds back into the model, improving accuracy over time.
Integrated directly with the client's existing AWS infrastructure and ERP system via Lambda functions, ensuring zero disruption to existing workflows during rollout.
Results
82% — reduction in processing time
94% — first-pass extraction accuracy
20hrs — saved per week
Stack
Python, LangChain, AWS Lambda, React