Incoming invoices or packing slips need to become structured data
For PDFs and scans, OCR plus extraction is standard. Microsoft Form Recognizer (Azure AI Document Intelligence), Google Document AI, or open-source like Donut work fine for SMB volumes.
Try this first
- 1Inventory document types: invoice, receipt, packing slip, contract. Build a separate extraction or train a custom model per type.
- 2Start with a prebuilt model (Form Recognizer 'invoice', Document AI 'invoice processor'). Often 80% out-of-the-box for standard NL invoices.
- 3Add a validation step: VAT equals net + rate, supplier id matches CRM, date is valid. Without validation, accounting inherits errors.
- 4For low-confidence cases: place in a review queue with PDF and extracted fields side by side. A human corrects in 30 seconds.
- 5Feed corrections back into your training set. After a few hundred corrections a custom model handles almost everything alone.
When to bring us in
Got a doc mix that doesn't fit prebuilt or a PII concern about cloud OCR, we can look at on-prem or EU-region options.
See also
- n8n: self-host or cloud?Self-hosted is cheaper at volume and keeps data local. Cloud removes ops burden.
- Zapier or Make: which fits better?Zapier is straight-line; Make handles complex flows with routers and iterators for less money.
- Power Automate Cloud or Desktop: which to use?Cloud for SaaS integrations and triggers. Desktop for RPA against legacy Windows apps without APIs.
None of the above fits?
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