Intelligent Document Processing at Scale

FinanceFlow, a growing fintech company specializing in small business loans, was bottlenecked by manual document review. Their team of analysts couldn't keep up with application volume.

The Challenge

FinanceFlow's document processing challenges:

• Manual extraction: Analysts manually reviewed bank statements, tax returns, and financial documents
• Inconsistent quality: Different analysts extracted different data
• Slow turnaround: 48-72 hours to process a complete application
• Scaling limits: Couldn't grow without proportionally adding staff

The Solution

We implemented an LLM-powered document processing pipeline that could understand, extract, and validate financial documents automatically.

Architecture Overview

The solution combined several AI components:

Documents → OCR → LLM Extraction → Validation → Human Review (edge cases)

Component 1: Document Classification

First, we built a system to automatically classify incoming documents:

• Bank statements
• Tax returns (various forms)
• Financial statements
• Supporting documentation

Component 2: Intelligent Extraction

Using a combination of RAG and structured prompting:

• Extract key financial metrics
• Identify anomalies and red flags
• Cross-reference across documents
• Generate summary reports

Component 3: Validation Layer

Automated checks for accuracy:

• Mathematical verification
• Cross-document consistency
• Format and completeness checks
• Confidence scoring for human review

Implementation Timeline

Phase	Duration	Deliverables
Discovery	2 weeks	Requirements, data analysis
Prototype	4 weeks	Working extraction for bank statements
Expansion	6 weeks	All document types, validation
Production	4 weeks	Full deployment, monitoring

Results

The impact on FinanceFlow's operations was significant:

• Review time: 45 minutes → 9 minutes per application
• Accuracy: 94% → 99.2% (with fewer human touches)
• Processing speed: 48-72 hours → 12-24 hours
• Capacity: 500 → 10,000 documents per day

Technical Highlights

Handling Edge Cases

The system was designed for graceful degradation:

• Low confidence scores trigger human review
• Unknown document types are flagged
• Handwritten notes are extracted separately
• Multi-language support for international documents

Security and Compliance

Financial document handling required strict security:

• SOC 2 compliant infrastructure
• Data encryption at rest and in transit
• Audit logging for all extractions
• PII handling controls

ROI Analysis

Investment payback was achieved in under 6 months:

• Development cost: $180,000
• Annual savings: $420,000 (reduced manual labor + faster processing)
• Revenue impact: $600,000+ (higher application volume)

Client Testimonial

"What used to take our team days now takes minutes. The Pelles engineers helped us build something that actually works in production, not just a demo. Our analysts now focus on complex cases while the AI handles routine extraction."

— CTO, FinanceFlow