Analysis of relevant trends, including why "insight engine technology is critical".Expert perspectives and strategic planning assumptions.Insight engines should be viewed as platforms on which multiple insight applications are provided and developed. Connectors and pre-index processing are used to gather and enrich data before it is indexed touchpoints and post-query processing are used to simplify the experience for people. They do this by connecting to varied sources and types of content (such as documents in content services platforms) and data (such as records in operational database management systems) in order to build an index of extracted data that can be queried by people and machines. They deliver information in context to people (content in context) and to support machine automation (data in context). Insight engines represent an evolution of search and natural language technologies (NLTs). They enable preexisting or newly synthesized information to be delivered proactively or interactively, in context, to digital workers, customers and others at timely business moments. Gartner defines insight engines as systems that apply relevancy methods to discover, describe, organize and analyze content and data. This comes after been positioned repeatedly as a Leader in the Gartner Magic Quadrant for Insight Engines, 2019, 2018, 2017 and in the Gartner Magic Quadrant for Enterprise Search in 2015. Sinequa is thrilled to be recognized as one of only four vendors named in the Leaders Quadrant in the Gartner' 2021 Magic Quadrant for Insight Engines, which evaluated 15 vendors total.
Sinequa Recognized as a 2021 Magic Quadrant Leader for Insight Engines
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