Document Intelligence Pipeline (IDP with RAG)

A financial institution processed thousands of multi-format documents daily—loan applications, financial statements, tax returns, and identity documents. Manual review was slow, error-prone, and created bottlenecks in loan approval workflows. Documents arrived in various formats (PDFs, scanned images, handwritten forms), with inconsistent quality and structure. The company needed an automated solution that could extract data accurately, validate information across documents, and flag inconsistencies for human review.

Designed and implemented a modular intelligent document processing (IDP) pipeline with retrieval-augmented generation:

Pipeline Architecture:

• Document Ingestion: Multi-format document intake with classification
• OCR Layer: Amazon Textract for text extraction from images and PDFs
• Extraction Engine: Custom NER models and template-based extraction
• Validation Layer: LLM-powered consistency checks and cross-document validation
• RAG System: Vector database for document context and historical patterns
• Human-in-the-Loop: Confidence scoring with intelligent routing to reviewers

Key Capabilities:

• Automatic document classification (loan apps, statements, IDs, tax forms)
• Multi-page document processing with relationship tracking
• Handwriting recognition for forms
• Cross-document consistency validation (e.g., income verification across pay stubs and tax returns)
• Confidence scoring for extracted fields
• Intelligent exception handling and human escalation
• Audit trail and explainability for all extractions

• 75% reduction in manual document review time
• 92% extraction accuracy across all document types
• 85% straight-through processing rate (no human intervention)
• 3-day reduction in average loan approval cycle
• $1.5M annual savings in operational costs
• Processed 100,000+ documents in first 6 months
• 40% improvement in data quality confidence
• 99.5% audit compliance rate