Structured Data Extraction

Extract structured data from scanned or digital bills across four complexity tiers, from simple invoices to multi-page documents with nested tables, multi-currency charges, and poor scan quality.

A finance team was manually processing bills and invoices that ranged from clean digital documents to barely legible scans with complex table structures. The volume was growing, accuracy requirements were high, and the existing process could not scale.

Challenge

The document complexity spans four tiers. Simple invoices are easy to parse. Multi-page documents with nested tables, multi-currency charges, and degraded scan quality are not. A single extraction approach cannot handle the full range efficiently. The team needed a solution that could adapt to document complexity while keeping costs manageable for high-volume, low-complexity documents.

What We Designed

We proposed two parallel tracks. The Vision LLM track renders bills as images, segments them into a grid, and processes each segment in parallel with a vision-capable model, followed by a validation pass. No model retraining needed. It handles edge cases well but carries higher latency (5 to 15 seconds) and token costs.

The Traditional OCR track fine-tunes a domain-specific model on annotated samples. Faster and cheaper to run, but requires retraining when new bill types are introduced. Both tracks route low-confidence extractions to human review, and every correction feeds back into the system automatically. Estimated effort ranges from 160 to 430 hours, depending on the approach and platform.

The Opportunity

The design gives the team a clear choice between flexibility and cost, with a shared human-in-the-loop layer regardless of which track they choose. Both paths produce structured data that flows directly into downstream systems, and the feedback loop means accuracy improves with every document processed.