Case Study: Large Financial Services Company achieves 50% review reduction and $70K cost savings with Reveal's Continuous Multimodal Learning

Visual Analytics and CMML Reduce Review Set to .1% of Docs

Large Financial Services Company faced a breach-of-contract litigation with an overly broad initial cull that produced roughly 125–140k documents, a tight production deadline, and limited attorney review resources. They engaged Reveal and its Continuous Multimodal Learning (CMML) approach to accelerate and prioritize review.

Reveal implemented Brainspace-powered CMML, training the model on a 400-document seed set and using Predictive Ranks plus Diverse Active Learning to surface the most relevant, diverse documents first. The process cut the review population from 140,000 to 70,000 (a 50% reduction), produced an initial 75% richness, saved about 1,750 review hours and over $70,000 in attorney fees, and enabled the client to meet the deadline.