Intelligent Document Processing Platform

Problem

Enterprise document automation lives or dies on trust: a wrong invoice line item posted to finance is worse than a document that waits in a review queue. The platform automates ingestion, classification, extraction, validation, and routing of high-volume business documents (vendor invoices, purchase orders, bills of lading, contracts, vendor onboarding packets, and customer quotes and RFPs) into ERP, procurement, and analytics systems, replacing manual data entry with LLM-based extraction and confidence-scored routing.

Approach

• Agentic pipeline with confidence gates. LangGraph orchestration with classifier, extractor, validator, and escalation nodes. Confidence thresholds route low-certainty fields to human-in-the-loop exception queues; clean documents post straight through.
• OCR feeds typed extraction. Azure AI Document Intelligence OCR output feeds GPT-4o (Azure OpenAI) structured field and line-item extraction against Pydantic-typed schemas, with per-field confidence scoring and deterministic fallback rules.
• Validate before finance. Every extraction is checked against vendor master data, two-way and three-way PO matching, and pricing rules before it reaches finance systems.
• RAG-grounded extraction. Azure AI Search vector retrieval over vendor-specific templates and historical documents grounds the extractor; PyTorch document-type classifiers handle routing.
• Eval as a release gate. Harnesses on Databricks with MLflow experiment tracking and model versioning measure field-level precision and recall, straight-through-processing rate, extraction cost per document, and model drift on every release. A regression fails CI.
• Operations. Docker-containerized microservices on Kubernetes, Azure DevOps CI/CD, Application Insights observability dashboards. Document taxonomies, field-level acceptance criteria, and exception-handling service levels defined with finance, procurement, and supply chain stakeholders.

Stack

• Orchestration: LangGraph and LangChain over Python 3.11 and FastAPI microservices.
• Models: GPT-4o on Azure OpenAI for extraction, PyTorch classifiers for document-type routing.
• OCR: Azure AI Document Intelligence.
• Retrieval: Azure AI Search vector retrieval over vendor templates and historical documents.
• Data and eval: Databricks with MLflow tracking, Snowflake, Kafka for the event-driven document flow.
• Frontend: React and TypeScript exception-review UI for human-in-the-loop approval.
• Operations: Docker on Kubernetes, Azure DevOps CI/CD, Application Insights.

Outcomes

• Agentic classify, extract, validate, escalate pipeline in production, replacing manual data entry with confidence-scored routing.
• Human-in-the-loop exception queues for low-certainty fields; straight-through posting for clean documents.
• CI-enforced release gates on field-level accuracy, straight-through-processing rate, and model drift.