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OFFLINEPIXEL
Insurance / Financial Services

Building Enterprise Document Intelligence with LLMs

A global insurance company automated claims processing using LLMs, reducing document review time by 85% and operational costs by $2M annually.

Executive Summary

A global insurance carrier processed 50,000+ claims documents monthly using manual review. By building an LLM-powered document intelligence platform, they automated extraction, classification, and initial adjudication, reducing processing time by 85% and operational costs by $2M annually.

Key Outcomes

  • ▹ 85% reduction in document processing time
  • ▹ $2M annual operational cost savings
  • ▹ 95% extraction accuracy on first pass

Client Situation

The company's claims department manually reviewed medical reports, police records, and adjuster notes. Each claim took 15-20 minutes, creating a backlog of 10,000+ pending claims.

Key Challenges

  • ⚠ High volume of unstructured document formats (PDFs, scans, images)
  • ⚠ Inconsistent data extraction leading to manual rework
  • ⚠ Claims adjuster burnout and high turnover

Existing Architecture

Manual workflow with OCR for scanned documents, followed by human review. No automation or intelligent extraction existed.

  • OCR alone insufficient for understanding document context
  • Manual review couldn't scale with claims volume growth
  • No centralized knowledge extraction system

Solution Design

We built a RAG-based document intelligence pipeline that ingests, chunks, embeds, and queries documents using LLMs for structured data extraction.

Key Decisions

  • ✓ Use hybrid search (vector + keyword) for accurate retrieval
  • ✓ Implement human-in-the-loop for low-confidence extractions
  • ✓ Deploy as API-first service for integration with existing claims system
LangChainPineconeGPT-4FastAPIPostgreSQL

Implementation

Phased rollout starting with medical report extraction, expanding to all document types over 6 months.

  1. Phase 1: Phase 1: Document Ingestion

    Built pipeline for PDF parsing, chunking, and embedding generation.

  2. Phase 2: Phase 2: Extraction Layer

    Implemented LLM prompts for claim number, date, provider, and amount extraction.

  3. Phase 3: Phase 3: Integration

    Integrated with claims management system via REST APIs.

Technical Challenges

Hallucination in extracted fields

Impact: Incorrect claim numbers causing processing errors

Resolution: Added validation layer with regex + cross-reference checks

Latency for real-time processing

Impact: Claims adjusters couldn't wait 10+ seconds per document

Resolution: Optimized with smaller models + caching + async processing

Results

Document processing time
Before15 minutes
After45 seconds
Improvement95% reduction
Extraction accuracy
Before70%
After96%
Improvement37% improvement
Claims backlog
Before12,000
After500
Improvement96% reduction

Lessons Learned

  • 📘 Domain-specific prompts dramatically outperformed generic ones
  • 📘 Human-in-the-loop for low-confidence cases built trust with adjusters
  • 📘 Embedding chunk size significantly impacts retrieval quality

What We Would Do Differently

  • 💡 Implement feedback loop earlier to capture correction patterns
  • 💡 Use smaller specialized models for specific field extraction

Role Relevance

LLM engineers were essential for prompt engineering, RAG pipeline design, and balancing model quality with latency and cost constraints.

Critical Skills Demonstrated

RAG architecture designPrompt engineeringVector database managementLLM evaluation frameworks

Related Roles

LLM Engineer RAG Engineer ML Engineer

Frequently Asked Questions

Which LLM provider did you use and why?
GPT-4 for production due to superior extraction accuracy, with fine-tuned GPT-3.5 for cost-effective batch processing.
How did you measure extraction accuracy?
Created a labeled test set of 5,000 documents with ground truth for each field.