Intelligent Document Processing with AI
Automate extraction, classification, and processing of business documents — invoices, contracts, forms — reducing manual data entry by 85%.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
Staff manually key data from invoices, purchase orders, contracts, and forms into ERP/accounting systems. A single invoice takes 3-5 minutes to process. Error rates average 2-4%. Document backlogs create payment delays and vendor dissatisfaction. Scaling requires proportional headcount.
After
AI extracts data from documents automatically with 95%+ accuracy. Documents are classified, routed, and entered into systems without human intervention for straightforward cases. Staff review only exceptions flagged by AI. Processing time drops from minutes to seconds per document.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Document Inventory & Prioritisation
1 weekCatalogue all document types processed by your organisation. Rank by: volume, processing time, error impact, and automation potential. Select top 2-3 document types for initial deployment (typically invoices, purchase orders, or receipts).
Configure Document AI
3 weeksSet up document processing platform (Azure Document Intelligence, AWS Textract, Google Document AI, or ABBYY). Train extraction models on your document formats — upload 50-100 sample documents per type. Define extraction fields, validation rules, and confidence thresholds.
Build Processing Workflow
3 weeksDesign the end-to-end flow: document ingestion (email, scan, upload) → AI classification → data extraction → validation → human review queue → system entry. Connect with your ERP/accounting system for automated posting. Build exception handling for low-confidence extractions.
Validate & Go Live
2 weeksProcess 500+ documents through the AI pipeline. Compare AI extractions against manual entry for accuracy. Tune confidence thresholds: high confidence goes straight through, low confidence routes to human review. Target: 80%+ straight-through processing on day one.
Expand & Optimise
OngoingAdd additional document types. Improve extraction accuracy through model fine-tuning. Build dashboards showing processing volumes, accuracy, and exception rates. Connect with accounts payable automation for end-to-end touchless processing.
Tools Required
Expected Outcomes
Reduce manual data entry by 80-85%
Achieve 95%+ extraction accuracy for trained document types
Process documents in seconds instead of minutes
Reduce data entry error rate from 2-4% to under 0.5%
Enable same-day processing vs. multi-day backlogs
Solutions
Related Pertama Partners Solutions
Services that can help you implement this workflow
Frequently Asked Questions
Modern document AI handles typed and printed text with high accuracy. Handwriting recognition is improving but less reliable — accuracy depends on handwriting clarity. For poor quality scans, pre-processing (image enhancement, deskewing) helps significantly. The key is testing with your actual document quality to set realistic expectations.
For standard document types (invoices, receipts), pre-trained models work well with 20-50 samples for fine-tuning. For custom or unusual formats, 50-100 samples per document type are recommended. The AI improves continuously as it processes more documents in production.
Ready to Implement This Workflow?
Our team can help you go from guide to production — with hands-on implementation support.