As technical lead at a US litigation-data firm, I built the NLP/ML pipeline behind high-stakes litigation delivery - turning a manual review bottleneck into an accurate, auditable, automated process.
High-stakes litigation meant millions of documents had to be classified and retrieved accurately and defensibly. Manual review did not scale, and errors carried real legal and financial risk under strict compliance regimes.
I architected an NLP/ML pipeline spanning Relativity, CloudNine Law, and Microsoft Purview, plus a speech-to-text pipeline for audio evidence. The design prioritized accuracy, auditability, and repeatability - aligning the AI roadmap with client needs alongside the CEO.
The pipeline processed 1M+ documents at 99% accuracy and speech-to-text at 99% accuracy, improved process efficiency ~30%, and cut retrieval time ~25% - while staying defensible under compliance review.
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