A financial services client was spending 2,400+ hours per year manually processing invoices, contracts, and compliance documents. Data entry errors were running at 4.2%, and processing delays were causing late payment penalties. We built an AI-powered document processing pipeline that reduced manual effort by 87% and achieved 99.1% extraction accuracy.
The Challenge
Before: Manual document processing workflow
PDF / Scan
30 min / doc
Error-prone
Manual
The Solution Architecture
After: AI-powered intelligent document pipeline
Power Automate
Document Intelligence
Classification & QA
ERP / Approval
How We Built It
Document Ingestion with Power Automate
Power Automate monitors shared mailboxes and SharePoint document libraries. When a new document arrives (PDF, scanned image, or Word file), it automatically triggers the processing pipeline. Documents are classified by type - invoice, contract, compliance form - using a custom Azure AI classifier trained on 500 labelled samples.
AI Extraction with Azure Document Intelligence
Azure AI Document Intelligence (formerly Form Recognizer) extracts structured data from documents using pre-built models for invoices and receipts, plus custom models trained on the client's specific contract templates. Key fields extracted: vendor name, amounts, dates, line items, payment terms, and contract clauses.
GPT-Powered Enrichment & Validation
Extracted data is passed through Azure OpenAI GPT-4 for enrichment: matching vendors to the master vendor list, flagging unusual amounts, detecting duplicate invoices, and summarising contract terms in plain English. The model also performs quality assurance on the extraction results, catching OCR errors and ambiguous fields.
Approval Workflow & ERP Integration
High-confidence extractions (>95% accuracy score) are auto-approved and posted directly to the ERP system via API. Lower-confidence items are routed to a human reviewer in a Power Apps interface, where they can verify and correct the AI's output. This human-in-the-loop approach ensures accuracy while minimising manual work.
Results
manual effort
accuracy
time per doc
year one
Technology Stack
Doc Intelligence
GPT-4
Orchestration
Review UI
Analytics
Lessons Learned
- Training data quality matters more than quantity - 500 well-labelled samples outperformed 2,000 noisy ones
- Human-in-the-loop is essential - even 99% accuracy means 1 in 100 errors; high-value documents need human review
- Start with high-volume, low-complexity documents - invoices before contracts, standard forms before custom layouts
- Monitor model drift - vendors change their invoice formats; retrain quarterly with new samples
"We went from dreading month-end invoice processing to barely noticing it. The AI handles 87% of documents end-to-end, and the remaining 13% are pre-filled and take 2 minutes instead of 30." - Head of Finance, Client
Ready to automate your document processing?
We'll assess your document volumes and build a business case for AI-powered automation.
Get a Free Assessment