DocuVerify: processing 40,000 documents per day with 96% AI accuracy

DocuVerify, a legal-tech company, processed contract review documents manually — a team of 12 reviewers working through a queue of 40,000 documents per day. Processing was slow, variable in quality, and a bottleneck for their client SLAs.

The challenge: documents were unstructured — scanned PDFs, hand-written notes, and digital originals mixed together. No single classification model would work. The solution required document-type classification, OCR for non-digital inputs, and LLM-powered review for each document class.

Simplileap designed a multi-stage pipeline: AWS Textract for OCR, a fine-tuned classification model to route documents by type, and document-specific LLM prompts with structured output schemas for each review category. Confidence thresholds below 0.85 routed documents to human review.

The human review queue became a training data source — every correction fed back into prompt refinement. Within 6 weeks of production deployment, accuracy on the automated segment reached 96.4%.

LangSmith observability provides complete trace visibility into every inference — which documents triggered human review, token cost per document type, and model latency. The VP Operations can see the pipeline health in real-time without engineering support.

The 12-person review team was redeployed to complex exception handling and client-facing quality assurance — work that creates more value than mechanical document review.