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AI Document Processing Case Study for Invoices and Orders

A workflow was implemented for automatic document data extraction with business-rule validation and human handling of exceptions.

Business type
Small equipment supplier and distributor
Project type
AI document automation
Outcome
63% faster incoming document processing
Docs/month
900-1200
Duration
6 weeks
Processing speed
+63%
Manual entry
-46%

Project summary

The client handled 900-1200 monthly documents (invoices, purchase orders, delivery notes).

Manual data entry overloaded admin staff and increased errors.

The goal was faster processing and better accuracy without replatforming the ERP.

Problem

  • Data entry took too long.
  • Input errors created avoidable correction loops.
  • Admin workload spiked at month-end.

Solution

  • OCR + AI extraction of key fields from PDFs and scanned documents.
  • Rule validation for supplier, amount, tax, and purchase order references.
  • Automatic staging-table insertion with an exception dashboard for manual review.
  • Accuracy tracking by document type for continuous model tuning.

Stack used

Python extraction worker Azure Document Intelligence Azure OpenAI post-processing SQL Server staging + validation rules Power Automate On-prem ERP integration connector

Measurable result

  • 63% faster average processing time per document.
  • About 46% fewer manual data-entry steps for admin staff.
  • Entry errors reduced by 28% in the first 6 weeks after rollout.

Implementation notes

Timeline
6 weeks (pilot + production)
Scope boundaries
Scope covered incoming financial and operations documents; outbound invoicing was out of initial scope.
Data/privacy
Documents processed via controlled services; storage and access aligned with the client’s internal policies.
Adoption
Admin team ran a 2-week parallel mode to validate output before switching fully to the new process.

What made rollout successful

  • Clear validation rules by document type.
  • Strong exception workflow instead of forcing 100% automation.
  • Weekly model tuning based on real error patterns.

Is document handling still slow and expensive?

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