75% reduction in manual screening
using AI-guided document reduction

Legal teams handling large-scale litigation, regulatory investigations, and compliance cases routinely face millions of documents to review. Traditional linear review — where attorneys manually screen each document — takes months, costs millions in professional fees, and is inherently prone to human error and inconsistency.

Envize needed technology that could dramatically reduce the volume of documents requiring human review while producing defensible, repeatable results that would hold up in court. The solution had to work across diverse document types, adapt to different case contexts, and improve accuracy over time as reviewers provided feedback.

The stakes were high: inaccurate document classification could miss critical evidence or produce privileged documents, with severe legal and financial consequences.

DSi's AI engineering team built machine learning pipelines using Gensim and Scikit-learn for intelligent document clustering and prioritization. The system uses novel data clustering techniques to group similar documents together, allowing reviewers to make decisions on document families rather than individual files.

A key innovation was the continuous active learning model: as reviewers classify documents, the ML models update in real time, improving accuracy with each decision. This creates a feedback flywheel where human expertise trains the AI, and the AI amplifies human capacity.

DSi built a unified workflow integration connecting ML models with existing review platforms through a Spring Boot and Flask backend, with Next.js dashboards providing real-time visibility into review progress, model confidence scores, and cost savings metrics. The enterprise-grade architecture ensures reliability even when processing millions of documents simultaneously.