When Fidelity National Financial approached us, they were processing millions of title documents manually. Errors were costly—a single mistake could delay a real estate closing or expose the company to liability. They needed an AI solution that could match human accuracy while processing at scale.
The Challenge
Title documents present unique challenges for AI systems:
- Highly variable formats across counties and states
- Mix of printed text, stamps, and handwriting
- Legal implications requiring near-perfect accuracy
- Complex tables and nested structures
- Documents spanning decades with varying quality
Our Approach
Phase 1: Deep Document Analysis
We spent the first month analyzing thousands of documents across different sources. This revealed patterns in document structure that informed our model architecture.
Phase 2: Custom Model Development
Rather than applying generic OCR, we developed specialized models for each document type:
- Title commitment extraction model
- Deed and mortgage parser
- Tax document processor
- Signature and notary verification
Phase 3: Confidence-Based Routing
Not every document needs human review. Our system assigns confidence scores and routes low-confidence documents to human reviewers while auto-processing high-confidence ones.
"The key wasn't just achieving high accuracy—it was knowing when we were uncertain. That confidence scoring system is what made the solution production-ready."
The Results
- 99.9% accuracy on processed documents
- 75% reduction in manual review time
- 60% faster document turnaround
- $2.3M annual savings in processing costs
Lessons Learned
- Domain expertise matters: Understanding title insurance workflows was as important as AI expertise
- Confidence scoring is essential: Knowing when to escalate to humans prevents costly errors
- Continuous improvement: The system gets better with every document it processes
- Change management: Training staff to work alongside AI required dedicated effort
The Fidelity implementation demonstrates what's possible when AI is thoughtfully applied to complex enterprise workflows. It's not about replacing humans—it's about augmenting their capabilities and letting them focus on what matters most.