Overview

Developed an end-to-end document processing pipeline that transforms unstructured documents into structured, actionable data. The system handles various document types including contracts, invoices, and technical specifications.

Challenges

• Variety of document formats: PDFs, images, scanned documents with varying quality
• Extraction accuracy: Maintaining high accuracy across different document structures
• Scale: Processing thousands of documents per day with consistent quality
• Security: Handling sensitive business documents with proper data governance

Solution

Built a multi-stage pipeline leveraging modern LLM capabilities:

1. Ingestion Layer: Robust document parsing with OCR fallback
2. Classification Engine: Automatic document type detection
3. Extraction Pipeline: Custom prompts optimized for each document type
4. Validation Framework: Confidence scoring and human-in-the-loop review
5. Integration APIs: RESTful endpoints for seamless integration

Results

• 85% reduction in manual document processing time
• 94% extraction accuracy across document types
• 3x throughput increase in document handling capacity
• Successfully integrated with existing enterprise systems

Technical Highlights

The system uses a combination of traditional NLP techniques and modern LLMs to achieve optimal results. Key architectural decisions included:

• Chunking strategies optimized for different document types
• Caching layer for repeated queries on similar documents
• Async processing for high-volume workloads
• Comprehensive logging and monitoring for debugging and optimization