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January 26, 2010

H5 launches EDGE product for enterprise governance engine

Allows organisations to cull, filter and classify email and other electronic data

By CBR Staff Writer

H5, a provider of automated information retrieval products and services, has released EDGE Classifier, the Electronic Data Governance Engine (EDGE) product for the enterprise.

H5 said that the EDGE Classifier provides companies the ability to make automated decisions on information using their existing technology infrastructures. It is an information classification application that runs on top of organisations’ information management systems to cull, filter, and classify email and other electronic data to meet information governance goals.

Building on the company’s core information retrieval technology, the new offering is designed to integrate with information management systems including enterprise search, content management, archiving, and e-discovery tools and platforms.

According to H5, the new offering features classification rules derived from its library of linguistic models; technical configurations that enable integration into existing enterprise systems; statistically valid measurement protocols that substantiate performance; and detailed documentation of classification criteria that enables clients to demonstrate that they have utilised a defensible and transparent process.

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The EDGE Classifier is built on Hi-Q Platform, an information retrieval development system for creating accurate search and classification applications. The Hi-Q Platform incorporates proprietary information retrieval software, relevance definition and linguistic modeling methodology, and calibration and validation software, H5 said.

Nicolas Economou, CEO of H5, said: “If companies don’t have a method that assures principled, accurate, document-by-document decisions on information, they’re leaving themselves open to substantial risk and cost. Because the H5 EDGE Classifier provides a proven means to achieve accurate assessments, our clients benefit from significant and measurable reductions in data volume and in associated costs and risks.”

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