Cognitive Search Brings the Power of AI to Enterprise Search

Forrester, one of the leading analyst firms, defines Cognitive Search in a recent report¹ as: The new generation of enterprise search that employs AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources. Here is a shorter version, easy to memorize: Cognitive Search = Search + NLP + AI/ML
Of course, “search” in this equation is not the old keyword search but high-performance search integrating different kinds of analytics. Natural Language Processing (NLP) is not just statistical treatment of languages but comprises deep linguistic and semantic analysis. And AI is not just “sprinkled” on an old search framework but part of an integrated, scalable, end-to-end architecture.

AI Needs Data, Lots of Data
For AI and ML algorithms to work well, they need to be fed with as much data you can get at. A cognitive search platform must access the vast majority of data sources of an enterprise: internal and external data of all types, data on premises and in the cloud. Hence the system must be highly scalable.

Continuous Enrichment
Cognitive Search uses NLP and machine learning to accumulate knowledge about structured and unstructured data and about user preferences and behavior. That is how users get ever more relevant information in their work context. To accumulate knowledge, a cognitive search platform needs a repository for this knowledge. We call that a “Logical Data Warehouse” (LDW).

The Strength of Combination
To produce the best possible results, the different analytical methods must be combined, not just executed in isolation of each other. For example, machine learning algorithms deliver much better results much faster if they work on textual data for which linguistic and semantic analyses have already extracted concepts and relationships between concepts.

Whitepaper-kmworld-07-2017Get your copy of the full paper here and learn more about current use cases of cognitive search and AI at large information-driven companies.

(1) Forrester Wave: Cognitive Search & Knowledge Discovery Solutions, Q2 2017
Read the full report on https://www.sinequa.com/forrester-wave-2017/

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Gartner Named Sinequa a Leader in Its Magic Quadrant for Insight Engines

As the CEO of Sinequa, I am proud that Sinequa was recognized as a leader in the recently released Magic Quadrant for Insight Engines 2017. Being a Gartner Leader, once again, underlines our continued progress that has led to this renewed leadership position in a Gartner Magic Quadrant. (We have previously been positioned as a leader in the Magic Quadrant for Enterprise Search.)  Gartner selects leaders for their “Completeness of Vision” and their “Ability to Execute” Good to see that others find our vision convincing and believe in our ability to realize it!

More reassuring still is the testimonial of our customers that led Gartner to state that “reference customers regarded Sinequa’s roadmap and future vision for its software to be particularly attractive. All indicated that those were significant reasons for choosing the software.”

As an established Cognitive Search platform, we’re continuing to evolve our vision and invest in enabling the largest organizations such as Airbus, AstraZeneca, Bristol Myers Squibb, Credit Agricole, and Siemens around the globe to get more value from their ever growing and diverse Enterprise data, as well as broadening the impact of search and analytics within the digital workplace of their employees.

According to Gartner:

“Insight engines apply relevancy methods to describe, discover, organize and analyze data. This allows existing or synthesized information to be delivered proactively or interactively, and in the context of digital workers, customers or constituents at timely business moments.”

Gartner-Magic-Quadrant-for-Insight-Engines-2017-Sinequa

Get your copy of the full report here and see why Sinequa is among the 3 leaders over the 13 vendors who participated in this Magic Quadrant.

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