Exterro Jumps into the Computer Assisted eDiscovery Race with Support for Full eDiscovery Lifecycle

Exterro, the leading provider of unified electronic discovery software, has announced the addition of Computer Assisted eDiscovery to the Exterro Fusion® eDiscovery suite. Fusion Predictive Intelligence™ allows legal teams to apply machine intelligence for the identification and categorization of electronically stored information (ESI) across the EDRM, from pre-collection through review. This latest advancement, now available in Fusion Zeta™, was developed in direct response to client and market demand for greater intelligence and cost reduction in the early phases of discovery.

What is significant about this announcement is that fact that Exterro is the first vendor to utilize and position machine intelligence across the entire eDiscovery lifecycle while the rest of the industry is focused on Predictive Coding and Technology Assisted Review (TAR) after collection and either just prior to or during the document review phase.  With this broader footprint that will touch more ESI sooner and more often within the eDiscovery lifecycle, Exterro has the potential to provide a much greater return on investment (ROI) for their clients than do many of the other predictive coding and TAR players in the market.

“Multiple eDiscovery software vendors offer products featuring “predictive coding” for streamlining costly, manual review. Yet our corporate clients are seeking faster access to the potentially relevant evidence and case facts much earlier in the process, so they can change the course of the matter prior to any evidence being collected,” said Ted Gary, senior product marketing manager at Exterro. “Fusion Predictive Intelligence takes predictive coding to the next level by applying machine intelligence across multiple phases of the e-discovery process.”

Exterro contends that Fusion Predictive Intelligence enables corporations to significantly reduce ESI volumes with advanced machine learning across multiple phases of the Electronic Discovery Reference Model (EDRM), including:

  • Early Case Assessment/Identification: Prior to collection, legal teams can now apply predictive algorithms to classify indexed documents and use the results to more accurately and rapidly assess the nature and scope of a matter.
  • Collection: Applied at the point of collection, legal teams can now collect and label only those documents identified by the predictive model to minimize data volumes and later stage review costs.
  • Review: Post-collection, predictive intelligence can be applied to precisely identify and code relevant documents based on the predictive model, sharply reducing manual review time and costs.

A complete version of the Exterro press release on Fusion Predictive Intelligence is available at: http://finance.yahoo.com/news/exterro-fusion-r-e-discovery-130000973.html

Later this week, I plan to discuss the significance of this announcement with Exterro,  and will provide a detailed review and opinion on the Fusion Predictive Intelligence offering.

 

About Charles Skamser
Charles Skamser is an internationally recognized technology sales, marketing and product management leader with over 25 years of experience in Information Governance, eDiscovery, Machine Learning, Computer Assisted Analytics, Cloud Computing, Big Data Analytics, IT Automation and ITOA. Charles is the founder and Senior Analyst for eDiscovery Solutions Group, a global provider of information management consulting, market intelligence and advisory services specializing in information governance, eDiscovery, Big Data analytics and cloud computing solutions. Previously, Charles served in various executive roles with disruptive technology start ups and well known industry technology providers. Charles is a prolific author and a regular speaker on the technology that the Global 2000 require to manage the accelerating increase in Electronically Stored Information (ESI). Charles holds a BA in Political Science and Economics from Macalester College.