Sinequa Expands AI Chops to Attack the New Digital Divide Among Enterprises

Says insights from company data will inevitably give competitive edge to enterprises that are good at getting them

Bill Stoller

June 29, 2017

4 Min Read
(Photo by Joe Raedle/Getty Images)

Paris-based Sinequa is a privately held software solutions company which provides its customers with insight engines for big data.

On July 27, Sinequa announced it is expanding its cloud AI for image recognition and video speech-to-text and natural language translation (NLP), adding Google Vision and Translate and IBM Watson Alchemy for Image Recognition to its platform. These additions bolster related capabilities already in place with the existing integration for Microsoft Azure Media Services for image and video treatment.

"The ability to ingest, understand, organize, and query digital content from multiple data sources whether on-premises or in the Cloud will be a key differentiator for enterprise solutions in general and Sinequa in particular," said David Schubmehl, IDC Research Director for Cognitive/AI Systems, in Tuesday's announcement.

Sinequa senior product marketing manager Scott Parker told Data Center Knowledge in an interview there is a rapidly expanding digital divide among Fortune 2000 companies. He believes large enterprises that harness the power of extracting insights from company data will inevitably gain a valuable edge over industry peers. Notably, Parker had previously worked within the IBM Watson group, after IBM purchased the software firm Vivisimo in 2012.

Sinequa, a founding sponsor of the Cognitive Computing Consortium, was recognized as a leader in the Gartner Magic Quadrant for Insight Engines in 2015, and again in 2017. Adding IBM Watson and Google Cloud capabilities appears to be a natural extension of an industry leading platform.

Sinequa Overview

Sinequa software uses Machine Learning algorithms for deep analytics of diverse data sets and user behavior to provide insight and suggest actions for users in their work context.


Source: Sinequa – (for all presentation slides)

Notably, Sinequa is agnostic when it comes to data sources. If an application and related data are deployed in multiple locations globally, and/or hosted in the cloud, Sinequa can crawl and index all locations to create a Logical Data Warehouse (LDW). Sinequa's grid architecture then provides an efficient way to scale, integrate, and distribute data while maintaining each customer underlying security rules and access.

Enterprise users often need the ability to extract information quickly and cost effectively from both on-premises and cloud-based sources.

According to The Forrester Wave in a recent report, "keyword-based enterprise search engines of the past are obsolete." The report explains, "Cognitive search is the new generation of enterprise search that uses artificial intelligence (AI) to return results that are more relevant to the user or embedded in an application issuing the search query."


Source: Forrester Wave Cognitive Search report - Q2 2017

Forrester defines cognitive search and knowledge discovery as:

"The new generation of enterprise search solutions that employ AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources."

Sinequa technology allows customers to translate text in a large number of languages, analyze and tag images, and transcribe video conversations which are embedded in rich media content.

Industry Verticals – Diverse Use Cases

Improving customer service, locating experts, avoiding duplicate work and assessing the competitive environment are ways Sinequa customers derive returns on their investment utilizing this technology.

In addition, when it comes to highly regulated industries a business case could be made for cost avoidance through utilizing AI, or "cognitive services," to avoid compliance issues and related penalties. Parker shared a simple example where video of a job site or factory floor could be automatically searched and screened for potential safety violations.


The power of overlaying Sinequa's software engine over data from 150-plus inputs can be leveraged for more complex tasks, including: financial institution fraud detection, insurance risk assessment, military intelligence, discovery of terrorist networks and money laundering, legal case management, pharmaceutical clinical trials and related research, are all examples of use cases.

Big Data is Big Business

Privately-held Sinequa has been profitable for five consecutive years, and doubled its revenues in 2016. Customers include governmental agencies and information-intensive organizations, including Airbus, AstraZeneca, Atos, Biogen, NASDAQ, European Union, IMF, Credit Agricole, Total, and Siemens.

Parker told DCK, "industry use cases developed by Sinequa can create a virtuous cycle where each customer deployment is better than the last." Sinequa software is a usage-based subscription model where the amount of customer data being analyzed dictates pricing – an elastic model similar to public cloud.

Adding IBM Watson and Google Cloud capabilities appears to be a natural extension of a successful technology. Sinequa has its own proprietary algorithms. However, this latest integration with IBM's cognitive services and Google's meta-data underscores how Sinequa also provides an easy path for large enterprise to utilize the latest AI innovations from partners under a single umbrella.

Read more about:


About the Author(s)

Subscribe to the Data Center Knowledge Newsletter
Get analysis and expert insight on the latest in data center business and technology delivered to your inbox daily.

You May Also Like