Is Your Enterprise Search Engine Made For You? Let’s Do A Fact Check

Enterprise Search Software has transformed the way organizations work by influencing their methods of storing & searching data. Companies across the globe rely on Enterprise Search Engines to retrieve relevant information from their internal pool of data. It serves as a secure, powerful, and time-saving application that facilitates easy file management, sharing, and search.

To qualify as a good enterprise search engine, an application must meet the following expectations:

1. Gather information from various data sources, formats & types.
2. Index, update & archive data.
3. Facilitate semantic search with autofill and result ranking
4. Allow users to refine the search using advanced filters.
5. Provide a user-friendly interface to collect & find data.
6. Limit accessibility for different users with permission sets.
7. Ensure data security.
8. As a bonus, allow an analysis of the search results and provide other intelligent search options.

Industry-leading search engines like IBM to Coveo, Elastic to Google, meet most, if not all, of these expectations and are striving continuously to innovate, improve & cater to multiple industries.

The question that we will explore here is — Can one solution serves all industries? Every industry has unique requirements, terms, jargon, and nuances. Does your enterprise search engine really know what you need for your job in your sector?

Let’s do a fact check on the most renowned enterprise search engines.

Coveo

Coveo is a cloud-based platform that leverages search & analytics to bring together data from multiple sources & deliver relevant information. As per Gartner’s Magic Quadrant for Insight Engines report, Coveo acquired Tooso in 2019 to improve the platform’s Machine Learning capabilities.

Key Benefits

1. High Speed: It helps you find information faster with autofill & relevancy-based result ranking features.
2. Seamless Integration: It integrates with third-party software like Salesforce, Microsoft (Dynamics) & ServiceNow for secure & easy indexing.
3. Personalization: Predicts your requirements to fetch the most relevant information.

Elastic

Elastic is a search engine software for workplace, website & app, supporting an organization’s customers & employees. The platform plans to extend its use cases by unifying data indexes from its Enterprise Search, Observability & Security product categories.

Key Benefits

1. Ease of Setup: You can download it on-premise or use it on cloud platforms, viz., AWS, Azure & Google Cloud.
2. Relevance Search Model: It deploys a Machine Learning model that facilitates fast & relevant search results.
3. Personalization: You can customize its search experience with plentiful options for Search UI, language & so on, to suit dynamic users.

Lucidworks

Lucidworks is known for its flagship product, Lucidworks Fusion, and add-ons like Predictive Merchandiser and Smart Answers. It boasts of providing end-to-end experiences with Connected Experience Cloud that draws upon its search & AI capabilities to gather information from every interaction & apply that to workflows.

Key Benefits

1. Data Analytics: The platform can analyze the data available, furnishing you with insightful information.
2. Advanced Search: It leverages AI & ML to empower its search by understanding user intent to fetch personalized & relevant information.
3. Reach: The software has a global reach, competing with big vendors.

IBM

IBM is leading the enterprise search engine industry with its product, IBM Watson. It is an open, multi-cloud platform that allows users to build an AI life cycle from scratch or use its pre-built enterprise apps to speed time-to-value.

Key Benefits

1. Smart Data Discovery: It gathers information on employees’ behavior & sentiments to improve work performance, conditions & relationships.
2. Research: It harnesses AI to enable rigorous & fast research. It also analyses complex data to fetch actionable & meaningful insights.
3. Customer Behavior: It helps companies understand their customer behavior to personalize their experiences at various touchpoints like Chatbots.

Google

Google Cloud Search, introduced in 2018, runs on the principal to help with internal (intranet) search. The platform plans to extend its functions by integrating with AI solutions like Google Assistant & Document AI.

Key Benefits

1. User-Specific: Unlike IBM Watson or Lucidworks, Google serves a specific audience, i.e., your employees for internal search & not your website customers.
2. Customer Experience: You can enjoy the flexibility of putting it to different uses.
3. Reliability: It benefits from the domain reputation and is trusted by global organizations.

Mircosoft

Microsoft’s Azure Search draws upon Microsoft’s own touchpoints that are either unique to this application or derived from Microsoft 365. It is expanding its functions & usage by integrating with content services products & introducing support for Conversational Search.

Key Benefits

1. Seamless Integration: It integrates with all Microsoft 365 applications & Project Cortex products like Microsoft Syntex.
2. Infinite Data Availability: It can pull information from a rich corpus of data, including Microsoft Outlook, Word & so on.
3. Innovation: Microsoft has dedicated experts to innovate & improve its search functionalities.

Sinequa

Sinequa ES is widely used for enterprise search, 360-degree (entity-centric) information views, unified enterprise content portals, expert finding, market intelligence, asset/portfolio management, news/trend analysis, information protection & data privacy. It aims to strengthen its ML & data source by integrating with Microsoft Graph.

Key Benefits

1. Fast & Easy Indexing: It has multiple embedded converters & built-in connectors that pull the content of different formats & sources.
2. Intelligent Search & Analytics: It uses natural language & machine learning technologies to enable efficient search, data mining & content analytics.

A Critical Analysis

All these popular enterprise search engines leverage ML to some degree, specifically, Natural Language Processing, to improve your data indexing & the overall search experience. While some are designed for both employee & customer use, others are meant for the organization’s internal use only. But there are two things that these search engines lack —

1. Industry Specialization: Almost all enterprise search engines are designed to suit any and all industries and have no vertical specialization. For any generic search engine to work successfully for your industry, the engine has to be ‘trained’ using long hours of professional services. For instance, if you were to hire a legal counsel with long experience in telecommunication for your energy business, they either won’t work out or will have a steep learning curve ahead of them.
2. Use-Case Focus: Large enterprises have multiple departments, each with unique needs but they all interface with the same enterprise search engine. Engineering, HR, Finance, Legal, Investor Relations all speak different languages and care about different data sets yet use the same search engine that pays no heed to their individual needs.

These two drawbacks pose some challenges for the users —

1. No Domain Understanding: The lack of industry focus means that these search engines can only understand the user’s question using general semantic understanding but cannot apply any domain rules. For e.g. the word “crater” has a different meaning in Geology and Chemical Industry so if the user asks the search engine “what causes a crater” then the context of the domain will be important in finding the right answer.
2. No Job Understanding: From employee management to product development or marketing teams, all have unique needs they all use the same generic search engine and receive generic answers as it lacks use-case focus. Without the use-case understanding, the search engine doesn’t know that the Investor Relation team puts a higher weight on market research data over customer contracts.
3. Broad Search with Irrelevant Results: Not all search engines use Natural Language Understanding (NLU) but the ones that do, use semantic relationships and basic ontologies to expand the search query (synonyms, hyponyms, hypernyms) to fetch all the closest matches. This increases the scope of the search but also results in hundreds of results that are irrelevant.

And that raises the question — what if your enterprise search engine learns about the industry, your domain, and your use-cases like a new employee joining the workforce? This is where Nesh comes in.

Nesh — An Enterprise Answer Engine

Nesh is designed to help businesses access their business & industrial knowledge in an efficient and smart way. She does 3 things well —

1. She already knows about your domain and can learn more by reading industry reference books and literature. She doesn’t need your time to train her.
2. She knows your job and is designed to solve specific use-cases.
3. She provides answers instead of links to documents. That’s why we call it an Answer Engine instead of a search engine.

Nesh is empowering chemical, power, and utilities, upstream, midstream & downstream oil & gas businesses, and investment banks & PE firms by improving their knowledge access capabilities. Want to know how Nesh can help your business? Connect with Nesh to request a demo.