Exterro Fusion® E-Discovery Platform Advances with Predictive Technologies across the EDRM

Fusion Predictive Intelligence delivers advanced machine learning to automate the identification and classification of electronically stored information (ESI) from pre-collection through review.

Portland, Ore., October 17, 2012 – Exterro®, a leading provider of unified electronic discovery software, announced the addition of predictive technologies to the Exterro® e-discovery suite.

The new capability, Fusion Predictive Intelligence, allows legal teams to apply machine intelligence to identify and categorize electronically stored information (ESI) across the Electronic Discovery Reference Model (EDRM), from pre-collection through review.

This advancement, available in Fusion Zeta, was developed in response to strong client and market demand for greater intelligence and cost reduction during the early phases of discovery.

“Many e-discovery vendors offer predictive coding to streamline expensive manual document review,” said Ted Gary, Senior Product Marketing Manager at Exterro.

“However, our corporate clients want faster access to potentially relevant evidence and case facts earlier in the process so they can make strategic decisions before data is even collected. Fusion Predictive Intelligence takes predictive coding to the next level by applying machine intelligence across multiple phases of the e-discovery process.”

Reducing ESI Volumes and E-Discovery Costs

Fusion Predictive Intelligence uses advanced machine learning to significantly reduce the volume of electronically stored information across multiple phases of the EDRM, including:

Early Case Assessment / Identification

Before data collection begins, legal teams can apply predictive algorithms to classify indexed documents and more quickly assess the scope and nature of a legal matter.

Collection

At the point of collection, predictive models help identify and label only the most relevant documents. This minimizes the volume of collected data and reduces downstream review costs.

Review

After collection, predictive intelligence can automatically identify and code relevant documents based on predictive models, significantly reducing the time and cost associated with manual document review.

“Predictive coding is one of the assisted review technologies showing strong promise for helping corporations manage document review, which is typically the most expensive phase of discovery,” said David Horrigan, Analyst at 451 Research.

“We believe Exterro is making a strong move by adding predictive capabilities to its Fusion platform, particularly by integrating these capabilities into a workflow that can be applied earlier in the e-discovery process before data collection begins. As organizations face exponential growth in electronically stored information, big data challenges, and increasing legal costs, we expect vendors to continue expanding predictive capabilities.”

“Addressing information governance and data reduction strategies at the outset of litigation—and across an organization’s entire legal portfolio—is critical for our clients,” said Bennett Borden, Partner at Williams Mullen.

“The biggest risk often comes from entering negotiations without a clear understanding of the scope, issues, and risks involved in a matter. Exterro’s predictive technologies provide another powerful tool to help clients reduce discovery costs not only for individual matters but across their entire legal portfolio.”

Availability

Fusion Predictive Intelligence is available immediately as part of Fusion Zeta, Exterro’s advanced data management application.

More information about the machine learning capabilities of the Exterro platform is available through Exterro.

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