AI Document Processing Workflow
This pipeline automates the intake, classification, field extraction, and routing of 500+ documents per day — replacing a 4-person manual review team with a 92% straight-through processing rate and same-hour routing for routine document types.
Note: This documentation covers the architecture and configuration of a representative deployment pattern. Specific client configurations and proprietary schema are omitted.
Pipeline stages
The workflow operates as a 5-stage event-driven pipeline:
- Ingestion — Documents arrive via email attachment, SharePoint watch folder, or REST API upload. A file validator checks format, size, and virus scan before passing to the queue.
- OCR & Pre-processing — Azure Form Recognizer extracts text layout, tables, and key-value pairs. PDFs are rendered at 300dpi; images are deskewed and contrast-normalised.
- Classification — A fine-tuned text classifier (DistilBERT) identifies document type from 12 classes. Confidence < 0.72 routes to the human review queue.
- Field Extraction — LangChain + GPT-4o extracts structured fields per document type using type-specific extraction prompts with JSON schema enforcement.
- Routing & Action — Extracted records are validated against business rules, then routed: auto-approved records POST to the target system API; exceptions queue for human review with an annotated preview.
Supported document types
| Type |
Sectors |
Avg extraction accuracy |
| Insurance claim forms | Insurance | 97.2% |
| Grant progress reports | NGO / Development | 94.1% |
| Purchase invoices | Finance, Procurement | 96.8% |
| ID documents (passport/national ID) | KYC, HR | 98.4% |
| Medical referral letters | Healthcare | 92.3% |
| Supplier contracts | Legal, Procurement | 89.7% |
{
"document_type": "invoice",
"vendor_name": "string",
"invoice_number": "string",
"invoice_date": "ISO 8601 date",
"due_date": "ISO 8601 date",
"line_items": [
{"description": "string", "quantity": "number", "unit_price": "number", "total": "number"}
],
"subtotal": "number",
"tax": "number",
"total_amount": "number",
"currency": "ISO 4217",
"payment_terms": "string",
"confidence": "0.0–1.0"
}
Routing rules
After extraction, each document passes through a configurable routing engine:
- Auto-approve: confidence ≥ 0.90 AND all required fields present AND amount within policy limits.
- Escalate to senior reviewer: confidence ≥ 0.72 but amount above threshold or policy exceptions detected.
- Human review queue: confidence < 0.72 OR required fields missing OR new document type.
- Reject: malformed document, virus detected, or failed validation checksum.
Confidence thresholds
| Score range |
Action |
SLA |
| 0.90 – 1.00 | Straight-through processing | <1 hour |
| 0.72 – 0.89 | Expedited human review | Same day |
| 0.00 – 0.71 | Standard review queue | 24–48 hours |
Deployment
The pipeline is containerised (Docker) and can deploy to:
- Azure Container Apps — recommended for existing Azure Form Recognizer users.
- AWS Lambda + S3 — event-triggered serverless variant for low-volume deployments.
- On-premise VM — for air-gapped environments with sensitive document types.
The sandbox below simulates the classification and extraction stages client-side for demonstration purposes. Production deployments use Azure Form Recognizer for OCR and GPT-4o for extraction.