Automating Invoice Processing with AI Agents
From 3 hours of manual data entry per day to fully automated invoice-to-ERP pipeline.
Sergiu Poenaru·February 20, 2026
The Problem
A logistics company processed 150+ invoices daily. Each invoice had to be manually read, validated against purchase orders, entered into their ERP, and filed. Three full-time employees spent their entire day on this.
Errors were common — wrong amounts, duplicate entries, missed invoices. Month-end reconciliation took 2 full days.
The Agentic Workflow
We built an end-to-end pipeline:
- Ingest: Invoices arrive via email or upload (PDF, image, or structured data)
- Extract: AI reads the invoice — vendor, line items, amounts, dates, PO numbers
- Validate: Cross-reference against purchase orders in the ERP. Flag mismatches.
- Approve: Auto-approve if within tolerance. Route exceptions to the right person.
- Post: Push validated data directly into the ERP system.
- File: Archive the original document with extracted metadata for search.
Key Design Decisions
- Multi-format handling: The agent handles scanned PDFs (OCR), digital PDFs, and even photos of invoices taken on mobile.
- Tolerance bands: Small discrepancies (under 2%) are auto-approved with a note. Larger ones are flagged.
- Vendor learning: The system learns each vendor's invoice format over time, improving extraction accuracy.
- Human-in-the-loop: Exceptions go to a review queue with the AI's best guess pre-filled.
Results (After 6 Weeks)
| Metric | Before | After |
|---|---|---|
| Processing time per invoice | 12 min | 30 sec |
| Daily manual hours | 9 hours | 45 min (exceptions only) |
| Error rate | 4.2% | 0.3% |
| Month-end reconciliation | 2 days | 2 hours |
Tech Stack
- OCR + Extraction: Claude vision for document understanding
- Orchestration: n8n workflow automation
- Validation: Custom rules engine + ERP API
- Storage: Supabase Storage + Postgres for metadata
- Monitoring: Slack alerts for exceptions and daily summary reports