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Document intelligence · Information extraction

Enterprise Invoice & Receipt Processing

Enterprise invoice review took three to five days per document, with extraction accuracy that varied by format and language. We built a multi-model extraction pipeline that validates its own output.

The challenge

Enterprise invoice processing workflows required three to five days of review per document, producing high operational cost, processing bottlenecks and inconsistent data extraction accuracy across multiple document formats and languages.

What we built

We implemented a document processing system combining Azure Form Recognizer, custom named-entity recognition models and GPT-4 to extract, validate and enrich invoice data from PDFs, images and scanned documents, with multi-language support.

  • Layout-aware extraction. Form recognition across PDFs, images and scanned documents.
  • Custom NER models. Domain-specific entity recognition on top of the general extraction pass.
  • Multi-model validation. Field values are cross-checked between models rather than trusted from a single pass.
  • Multi-language support. Documents processed across differing languages and formats.
  • Multi-tenant architecture. Isolation supporting diverse organisational requirements.

Results

90%+
reduction in processing time, from 3–5 days to minutes
95%+
field extraction accuracy through multi-model validation
99.8%
processing reliability with comprehensive error handling

Technology

  • Azure Form Recognizer
  • GPT-4
  • Custom NER models
  • Multi-language

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