Whitfield & Associates, a 120-attorney litigation firm, was drowning in document review. A single case could involve 500,000+ documents, and their review process relied on contract attorneys manually reading and categorizing each one. The cost was staggering — a recent antitrust case had generated $2.3M in document review fees alone. Existing e-discovery platforms offered basic keyword search and some machine learning capabilities, but none could handle the nuanced categorization that Whitfield's practice required. Their cases often involved technical subject matter (patent litigation, trade secret disputes) where generic ML models performed poorly. The firm needed a system that could learn from their attorneys' expertise, handle multiple document formats (PDFs, emails, spreadsheets, images), and provide defensible results that would withstand scrutiny in federal court.

We built a document intelligence pipeline that combined traditional NLP techniques with large language model capabilities, creating a system that could understand documents the way an experienced attorney would. The ingestion layer handled OCR for scanned documents, email threading for communication chains, and metadata extraction for all supported formats. We built a unified document model that preserved relationships between documents (email threads, attachment hierarchies, version chains). The classification engine used a multi-stage approach: initial automated categorization using fine-tuned models, followed by active learning where attorney feedback improved the model in real-time. We implemented a confidence scoring system that automatically routed low-confidence documents to human reviewers while allowing high-confidence classifications to proceed. The review interface was designed with attorney workflows in mind. Documents were presented with relevant context, highlighted key passages, and suggested privilege designations. Reviewers could approve, modify, or reject classifications with a single click, and each action fed back into the model. We implemented comprehensive audit trails and model explainability features, ensuring that every classification decision could be explained and defended in court.