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AlphaLit Announces Partnership with Content Analyst Co. to Integrate Robust Analytics Into E-Direct 3.0 Review Platform

Huntingdon Valley, PA, January 28, 2012 --( AlphaLit, a nationally recognized full service provider of litigation support services, today announced its strategic alliance with Reston, VA-based advanced analytics and conceptual search leader, Content Analyst Company. The combination of Content Analyst’s advanced analytics engine, CAAT, for exploring and evaluating unstructured documents, emails, and other text records with AlphaLit’s longstanding litigation support expertise provides clients with tremendous advantages for success. “In addition to our cutting-edge tools and proven practices, Content Analyst’s technology will help us offer a common-sense approach to document metrics that appeals to clients,” says founder Dale Drury.

Content Analyst’s patented advanced analytics technologies are based on Latent Semantic Indexing, which can work across languages and platforms and are used to address electronic discovery, records management, social media analysis and government intelligence matters. "We are pleased to welcome AlphaLit, a proven industry leader with a great reputation for providing top-quality solution to their clients, to our growing network of partners that are leveraging," says Kurt Michel, president and CEO of Content Analyst Company. “AlphaLit’s thoughtful implementation of the CAAT email threading and near duplicate will allow them help their clients efficiently manage the growing collections of documents involved in today’s matters. The full integration of CAAT’s advanced analytics into the E-Direct platform will raise the bar for other review platforms.”

AlphaLit will leverage insights from CAAT’s metrics to address near-duplication and foster e-mail threading for more efficient bulk tagging. This new partnership will bolster the defensibility of its legal teams, enhance its one-click quality control methods and potentially lower client costs. The Intuitive search capabilities and clustering produced by AlphaLit’s unique filtering “suggestions” will, for example, provide results exponentially faster yielding greater understanding and better outcomes.

Contact Information
Michele Evans

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