Document Processing Automation

The Problem

A mid-size insurance brokerage was drowning in paperwork. Every new policy required staff to manually review 15–30 pages of client documents, extract key data points, cross-reference against carrier requirements, and flag missing information. The process took 5+ hours per submission and was error-prone—missed fields meant back-and-forth delays with clients.

Pain points

• Manual extraction — Staff copying data from PDFs into spreadsheets
• Inconsistent formats — Documents from 40+ carriers, each with different layouts
• Error rates — ~12% of submissions returned for missing or incorrect data
• Backlog pressure — Peak season meant 3–5 day turnaround times

The Intervention

We built a document processing pipeline that combines OCR, structured extraction, and validation rules:

Technical approach

1. Ingestion layer — PDF/image upload with automatic page classification
2. Extraction engine — GPT-4V for complex layouts, Claude for text-heavy docs, with fallback to traditional OCR for simple forms
3. Schema mapping — Carrier-specific field mappings with confidence scores
4. Validation rules — Business logic checks (date ranges, coverage limits, required fields)
5. Human-in-the-loop — Review queue for low-confidence extractions

Architecture

Upload → Classification → Extraction → Validation → Review Queue → Export
           ↓                  ↓              ↓
      Page Type ML       LLM + OCR      Rules Engine

Stack

• Frontend: Next.js dashboard with drag-drop upload
• Backend: Python FastAPI, Redis queues
• AI: GPT-4V, Claude 3, Tesseract fallback
• Storage: S3 + Postgres
• Infra: AWS Lambda for extraction workers

The Outcome

Before → After:

• Processing time: 5+ hours → 30 minutes
• Error rate: 12% → 2.3%
• Staff capacity: 8 submissions/day → 35 submissions/day
• Peak turnaround: 3–5 days → Same day

ROI highlights

• $180K annual savings in staff time (equivalent to 2 FTEs)
• 4× throughput increase without adding headcount
• Client satisfaction up 40% (measured via NPS)

Key Learnings

1. Hybrid approach wins — LLMs excel at messy layouts, but traditional OCR is faster and cheaper for standardized forms
2. Confidence thresholds matter — Setting the right threshold for human review balances accuracy vs. throughput
3. Carrier-specific training — Fine-tuning extraction prompts per carrier format boosted accuracy 15%

Engagement type: AI Readiness Sprint → Production build
Timeline: 6 weeks from kickoff to production

This case study illustrates our capabilities with a representative scenario. Details have been generalized to protect client confidentiality.