Case Study: International Government Investigation achieves 85% review population reduction and over $750K in cost savings with Reveal

International Government Investigation - Customer Case Study

International Government Investigation needed to manage a 12.5 million–document review across multiple languages under a tight production deadline after overly broad keyword culling left 1.8 million documents for relevance review. They engaged Reveal, using Reveal’s Brainspace technology and Continuous Multimodal Learning (CMML), to address the scale, multilingual data, and time pressure.

Reveal trained CMML on 60,000 previously reviewed documents and applied Predictive Ranks and patented Diverse Active Learning to prioritize review. As a result Reveal reduced the review population from 1.8 million to 280,000 documents (≈85% reduction), increased relevance richness to 3.7% (over 270% improvement vs. keyword search), and saved about 19,000 review hours—over $750,000 in attorney fees.