Sinequa Introduces New Version of Cognitive Insight Platform

Sinequa  has announced the general availability of Sinequa ES Version 10. Powered by machine learning capabilities, the new release is aimed at delivering deeper analytics of contents and user behavior, and continually improving relevance of information to users in their work environments.

To achieve the move into the world of cognitive computing, with this new version, Sinequa has integrated the Spark platform in its distributed architecture and implemented machine learning algorithms on Spark within the core of its product. The machine learning algorithms continually analyze and enrich the content of the Sinequa Logical Data Warehouse. 

According to Alexandre Bilger, CEO, Sinequa, data-driven organizations need to rely on intelligent and self-learning systems to analyze data and find valuable information for their employees, in order to increase their productivity and job satisfaction, and the company’s competitiveness. The machine learning capabilities help achieve these goals by including collaborative filtering and recommendations, classification by example, clusterization and similarity calculations for unstructured contents, and predictive analysis.

With more than 150 ready-to-use connectors, Sinequa says it is continuing to broaden connectivity to enable extraction of valuable insights from enterprise applications, Hadoop and cloud environments.

In addition, with the release of Sinequa ES V10, Sinequa is now a “native resident” of cloud platforms, such as Amazon Web Services and Microsoft Azure, and industry-specific dictionaries and ontologies from partners such as Scibite and Linguamatics have been integrated for customers in life sciences and healthcare.

Google Vision and Microsoft Azure Media Services are also being used to deal more effectively with images and videos, and Google Translate is being used for automated translation of more than 100 languages.

For more information about Sinequa ES V10, visit www.sinequa.com/insight-platform.

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