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Enterprise search software comparison

Enterprise search has always been both a necessity and a challenge, and vendors have sought to bring improvements to the market by adding AI to their products. Here's an overview.

The evolution of enterprise search has been, in recent years, asymptotic -- always approaching but never quite reaching the level of accuracy and usability that's really needed.

More than a decade of big promises and tepid results has characterized the market, but the infusion of AI into enterprise search software is an effort by leading vendors to change that.

It's difficult to compare these vendors side by side, because each product addresses different aspects of Enterprise search and does so with different strategies. However, a rundown of the best features underscores the diverse range of search approaches.

IBM Watson Discovery

Big Blue's strategic approach to smart search has been to address the vast quantities of dark data building up in the enterprise -- content such as document files, Excel tables, PowerPoint presentations, emails and other formats that aren't accessible by keyword-based search because they aren't tagged.

Watson Discovery solves the problem with AI-driven natural language processing, reading unsurfaceable files and presenting them to the user. It is a tool set available in both cloud and on-premises versions that businesses can integrate into other applications. Users can teach the Discovery AI what is useful and what isn't, making it not only more efficient at search but also able to learn users' preferences. A multilabel classifier is built into this process, so the user is able to teach Discovery what to look for while providing an indexing system that Discovery can likewise learn and use autonomously.

Watson Discovery includes document connectors for Box, Salesforce, SharePoint and other platforms. With Cloud Pak for Data Integration, Discovery can run on any public or private cloud.

Salesforce Einstein Search

Augmenting Salesforce Global Search, Einstein has improved the CRM giant's enterprise product by applying machine learning to fine-tune search's context sensitivity within the CRM. Introduced in September 2019, Einstein Search identifies relevant user-specific data points that enable it to self-train on the user's intent. The user writes a query, and Einstein is increasingly sensitive to what the user is really going for.

Einstein Search can further narrow search results by learning a user's operational boundaries and focusing the search on them -- specific vertical industries or geographical territories, for instance. The system does not discard other search results, but the AI moves the bounded ones to the top of the list.

Microsoft Search

Microsoft's enterprise search software unites the AI technology already embedded in Bing with the personalized insights engine of Graph. Microsoft's approach to improving its search product has been incremental, and several prominent increments surfaced at Ignite 2019, including Project Cortex -- the much-anticipated knowledge network that is due for release in summer 2020.

These include attribute sensitivity in people search that does a smart check for misspelled names, geocentric localization and automated acronym search to add acronyms as search terms whether the user includes them in a query or not.

As with Watson Discovery, natural language processing and contextualization are included. An instant query prediction feature prioritizes search results based on other work immediately in progress. The context of a search is further defined by referencing relevant documents that users receive from others.

Google Cloud Search

The granddaddy of all search engines pioneered the use of AI in a search engine by employing machine learning to study user queries en masse, learn the structures of the most effective ones and return suggested query improvements to the user. It is available standalone or embedded in G Suite and makes the level of accuracy and relevance its users experience on the web possible -- at least in theory -- within the enterprise.

Google Search comes with more than 100 connectors and integrates with collaboration, content management and data storage platforms such as Box, Azure Data Lake, SharePoint, Amazon S3 and Microsoft OneDrive; databases such as Oracle, PostgreSQL and MySQL; and CRM systems such as Salesforce and SAP. It can even integrate with other search engines.

Amazon Kendra

It's unsurprising that the tech giants have all invested deeply in AI to enhance their enterprise search software, but innovations can be found in less prominent players as well.

Amazon announced Kendra in December 2019, and it likewise exploits natural language processing and machine learning to enable comprehensive queries, formatted not so much to surface content but to get specific answers to specific questions. In the description on the AWS webpage, an example of a question it can answer is, "How long is maternity leave?" which yields the response, "14 weeks." It is available as a console application and can also be adopted in the form of APIs that businesses can embed in other applications.

Kendra can initiate domain-specific searches based on the content of a query, such as focusing a pharmaceutical question on pharmaceutical sources, IT questions on IT sources and so on, enriching the quality of results and filtering out irrelevant ones.

Like Google Cloud and Watson, it offers connectors to data sources such as Box, OneDrive, Salesforce, Dropbox and others.

Lucidworks Fusion, Digital Workplace and Digital Commerce

It's unsurprising that the tech giants have all invested deeply in AI to enhance their enterprise search software, but innovations can be found in less prominent players as well.

Earning praise from both Forrester and Gartner, Lucidworks has specialized in enterprise search, and its Fusion platform enables user-specific engine training similar to that of Einstein Search. The product is meant to be built into custom applications, and its developer-friendly tools include customizable UX components. Non-developers can also use the platform to build apps with a visual self-guided interface.

Fusion features fluid deployment of machine learning models, exploiting existing ones while accepting custom models built with Python, TensorFlow, scikit-learn and spaCy. Businesses can integrate Fusion with IBM Watson and conventional file systems. Lucidworks also offers custom integration as a service.

Lucidworks also has a Digital Workplace application, which is in essence a predictive answer engine, merging AI-driven search with personalization utility and collaboration features. And its Digital Commerce system offers an AI-driven personalized customer experience that takes the customer journey into account when defining the context of a query.


AlphaSense specializes in search of unstructured and fragmented data -- such as email, text and social media data -- and businesses can integrate the software with a number of platforms and applications via its API set. The application depends heavily upon natural language processing and granular document classification, using AI-derived synonyms of user-provided search terms to enrich its search. Its AI architecture is scalable -- with algorithms training on billions of data points -- yielding refined performance tuning with significant noise reduction.

Targeted primarily at the financial and healthcare verticals, it also has a market intelligence engine that scopes results within selected domains -- as Amazon Kendra does -- and features a sentiment analysis model that can detect shifts in content tone in search results output.

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