RenewData, a full-service eDiscovery provider that offers the industry’s broadest spectrum of review acceleration solutions, today announced another addition to its Language-Based Analytics family of offerings: Language-Based Review Acceleration.
A key feature in this offering is Cross-Document Knowledge Extraction, which is unmatched in the industry. The output of other review solutions is simply a subset of documents from the collection that might be relevant, but which requires additional analysis in order to learn more about each document’s content. Cross-Document Knowledge Extraction adds deeper insight into these results, yielding a better categorization of results that can lead to strategic advantages and additional cost savings.
“The objective of document review is really twofold: understanding what documents may be relevant and understanding what those documents are about,” said Andy Kraftsow, chief scientist for RenewData. “Today’s technology solutions are adept at addressing the former, but fall short on the latter. RenewData’s Language-Based Review Acceleration now addresses both critical objectives, establishing it as the next generation of Technology-Assisted Review.”
Language-Based Review Acceleration is uniquely able to deliver Cross-Document Knowledge Extraction because it leverages language to understand content. As a result, it delivers:
Language-Based Review Acceleration is an alternative to artificial intelligence-powered solutions that use brute technical force and pattern matching to “predict” document relevance. It delivers similar time and cost savings as these alternatives, as well as Cross-Document Knowledge Extraction, superior transparency into coding decisions, better control and auditability and the ability to re-use work product from one project to the next.
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