Am Law 100 Firm Uses New Technology to Find More Relevant Documents and to Find Them Faster

Contact: Warwick Sharp, Vice President, Marketing and Business Development, Equivio Inc.
(800) 851-1965 | info@equivio.com | www.equivio.com

In today's litigation environment, completing discovery in a timely and cost-effective manner can seem an impossible task. The volume of discovery documents has risen exponentially over the past few years, and litigation budgets simply can't grow at the same rate.

Of even greater concern, quantity impacts quality, with the sheer volume of documents and emails inevitably resulting in errors in the review process.

New applications of machine learning technology can better leverage a human's subjective understanding of textual content. Use of these applications in litigation can potentially enhance the productivity, accuracy, and consistency of manual document review. While it makes good sense in theory, the key question is — do these tools actually work in practice?

In a pilot study recently conducted by Epiq Systems with an Am Law 100 New York-based firm, the Equivio>Relevance™ application was tested under controlled conditions to compare its analysis of an actual discovery document collection to the results of a purely human-based review. The trial offers dramatic insight into the potential benefits of augmenting human review with this new generation of technology.

The case study Am Law 100 Firm Uses Equivio>Relevance™ to Find More Relevant Documents and to Find Them Faster — an Epiq-Equivio Case Study discusses the surprising results of the pilot case. The case study is published here for the first time.

In this case study, you'll learn how the use of Equivio>Relevance to automatically prioritize documents by relevance:

• Enables the legal team to review most of the relevant documents at the onset of the review.


• Ensures consistent document classification across the entire collection.


• Identifies errors and discrepancies in responsive coding.