Equivio, a provider of analytical solutions for eDiscovery, today announced that global law firm Squire Sanders (US) LLP has received the 2012 Most Innovative Use of Technology in a Law Firm award by Law Technology News (LTN). The firm was selected for its Intelligent Discovery Process (IDP) approach to eDiscovery.
As a part of the Intelligent Discovery Process, Squire Sanders utilizes Equivio's Relevance application for predictive coding, together with other technologies and innovative workflow design, to engineer a highly effective and defensible eDiscovery strategy.
“We congratulate Squire Sanders on winning the LTN innovation award," said Amir Milo, CEO of Equivio. "As a pioneer among law firms in understanding the need to harness predictive coding to reduce review volumes and costs, Squire Sanders is at the forefront of redefining the way litigators perform electronic discovery."
The aim of Squire Sanders' Intelligent Discovery Process was to develop defensible methods for reducing the volume of billable hours associated with document review projects, while ensuring the quality of document review. Using Equivio's predictive coding technology, in conjunction with other technologies and its innovative workflow, Squire Sanders’ IDP has saved dozens of Squire Sanders' clients millions of dollars, while at the same time enhancing the quality, defensibility and transparency of the eDiscovery process.
"Equivio's predictive coding technology is a major component in our intelligent discovery workflow," said Robert Singleton, Director of eDiscovery and Data Management at Squire Sanders. “Using this technology as the cornerstone of a broader process, we are able to minimize the cost of human review while defensibly ensuring that the vast majority of relevant documents are discovered."
Equivio's Relevance application leverages predictive coding technology to organize a collection of documents by relevance. The application is "trained" by one or more attorneys to find relevant and privileged documents. Following this initial training, Relevance uses statistical and self-learning techniques to calculate graduated relevance scores for each document in the data collection. This enables informed early case assessment, precise data culling, prioritized review and systematic QA for human review.
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