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A drawing of a maze and a path through it, symbolizing search engines navigating data mazes

Enterprise search 7 Best Enterprise Search Engines for Navigating the Data Maze An organization that cannot efficiently handle its data will not be able to reach its peak potential. Office workers can spend as much as 19% of their total work week just looking for the information needed to do their jobs, according to a McKinsey report. Enterprise search engines are the solution for this productivity bottleneck. An organization’s body of knowledge comprises data in different formats and media. Tables, reports, emails, spreadsheets, videos, and images—the list goes on. It can be exhausting to navigate all of this manually. Luckily, enterprise search software makes indexing, searching, and displaying specific content to authorized users in the organization possible. The advantages of using enterprise search solutions include improved productivity, greater efficiency, and employee satisfaction. ✶ Find out how enterprise search improves internal communication A Quick Look at the Best Enterprise Search Engines for Navigating the Data Maze Don’t let mountains of data overwhelm your business. There are enterprise search tools that make knowledge management and navigating the data maze a convenient exercise. Let’s look at the most popular and useful ones. Best Overall Enterprise Search Engine: UnleashBest for Lightning-Fast Search: AlgoliaBest for Open-Source Scalability: Apache SolrBest for Distributed Search and Analytics: ElasticBest for AI-Led Relevance: CoveoBest for Digital Knowledge Management: YextBest for Integrations With Minimal Development: Addsearch 1. Unleash: Best Overall Enterprise Search Engine What is Unleash? Unleash is a cloud-based enterprise search platform that allows you to search across all of your organization’s apps and knowledge library and find all the information in one place. How Does Unleash work? Unleash works by connecting to your company’s entire knowledge database. Its enhanced information findability means all your searches will have quick results and data silos are broken down. With the power of AI and semantic search, the Unleash search engine is smart enough to consider synonyms and related concepts to deliver relevant information. It also works on various platforms: Embedded in the company portalThrough a fully kitted web and desktop appAs a browser extensionVia a Slackbot ProsCan perform a semantic search for related keywords and synonymsSeamlessly integrates with the company portalsSaves workers’ time and energyConsRequires a subscription FAQ 2. Algolia: Best for Lightning-Fast Search What is Algolia Search? Algolia is software that allows anybody to create intuitive and dynamic digital search and discovery experiences. So why Algolia? Its flexibility means it can be used for both customer- and employee-facing websites or applications. How Algolia Search Works To use Algolia search, you don’t need to be a tech wizard. This cloud-based software has free and paid versions, and both are easy to set up. You may be wondering if Algolia is open-source, and it's not, but it is available under a commercial SaaS license. The chief selling point is adaptive learning with AI, called NeuralSearch. This, along with the natural language understanding functionality, means Algolia is used to deliver search results even without keywords being used. ProsYields speedy search resultsUses AI for adaptive learningCan understand search queries even without keywordsConsRequires a level of technical proficiency FAQ 3. Apache Solr: Best for Open-Source Scalability What is Apache Solr? Apache Solr is an open-source enterprise search platform built on Apache Lucene. So why Apache Solr? It's optimized for high-volume traffic and is highly scalable and fault-tolerant. You can also use Apache Solr to navigate full-text searches on your platform, as well as additional functions like: Distributed indexingReplication and load-balanced queryingAutomated failover and recoveryCentralized configuration How Apache Solr Works It’s fairly easy to get started once you know how to install Apache Solr, which Apache Solr has an extensive guide on how to do it. It’s free to download, open source, and has a wealth of learning material. Because of its distributed indexing and filtering, it's more straightforward to identify and isolate the users and content you are searching for. Apache Solr can scan text files within its indexes in the following formats: JSONXMLCSVBinary over HTTP Results are received via JSON, XML, CSV, or binary results. ProsCommunity-supported and freeEasy to isolate users and content with search indicesOffers tutorials for new usersConsScaling up the enterprise search solution is complicatedSpecialized for text search, not multimedia search FAQ 4. Elasticsearch: Best for Distributed Search and Analytics What is Elasticsearch? Built with Java, Elasticsearch is a NoSQL database that allows stored data to be read from your disk when needed and the heap is basically used as working memory and should account for about 50% of available RAM. The popular tech stack for knowledge management is named ELK (Elastic, Logstash, Kibana) because of how pivotal Elasticsearch's software is. The Elasticsearch Relevance Engine (ESRE) uses both artificial intelligence (AI) and machine learning (ML) to deliver fast and reliable search results. How Does Elasticsearch Work? Elasticsearch's all-inclusive design is meant to be used for easier, more efficient searches. So, why use Elasticsearch? It enables ambitious game developers to build any kind of search application or data management program they can imagine. You can install it using their hosted site, with their various install packages, or using their Java version. The most simple would be to get an Elastic Cloud account, choose your tools, and build the optimal search experience for users. However, Elastic struggles to perform as efficiently when searching through huge datasets. ProsCompatible on every platformEasy to create backupsFlexibility of data sourcesConsLimited in terms of customization and security features Can be challenging to learn FAQ 5. Coveo: Best for AI-Led Relevance What is Coveo Search? Coveo is a cloud-based, intelligent enterprise search platform. It uses AI recommendation and personalization to increase the relevance of every user’s experience. How Does Coveo Search Work? Coveo is a secure and scalable enterprise search solution. If you're wondering if Coveo is any good, it's worth noting that it uses connectors and APIs to find results regardless of format, making it a versatile solution. The easy indexing and unified search experience mean content can be accessed no matter where it is located. Coveo’s enterprise search platform’s pricing is based on a subscription model with flexible payment plans. Cross-platform compatibility lets Coveo integrate seamlessly with your company’s tech stack and simplify searching for content. ProsYields results regardless of the content’s location or formatConnects data sources through APIs and connectorsGives AI-enhanced search results based on contextConsRequires a subscriptionDisplays unsorted results with no option to sort them FAQ 6. Yext: Best for Digital Knowledge Management What is Yext? Yext lets users create end-to-end digital experiences that result in smoother, faster enterprise search results. How Does Yext Work? Yext has many features that allow it to analyze data, organize your content, and deliver direct answers to any search queries from customers or employees, depending on your use case. This AI-powered enterprise search engine uses natural language understanding and semantic search to deliver results on your website, app, or any other platform. You can also push your structured data from Yext to third-party search engines. If you’re wondering if Yext is worth it, you can try a free trial to test if it fulfills your needs. ProsEmploys neural network to respond to keyword-based queriesOffers free trial Can build your own search networkConsExpensive enterprise search solutionLimited data analytics functionality FAQ 7. Addsearch: Best for Integrations With Minimal Development What is AddSearch? AddSearch is a dedicated enterprise search platform for companies that run a website. This smart site search solution is designed to deliver stunningly fast results on any website or application. How Does AddSearch Work? AddSearch’s site search feature uses a self-learning algorithm to learn site-visitor patterns and adjust search results accordingly. The look and feel of its multi-domain enterprise search engine can be customized according to your site’s aesthetic. Your site search results will always be current, thanks to a web crawler. This software is especially useful for those running e-commerce sites. ProsHighlights popular results from past searchesMulti-domain search platformConsThe web crawler causes difficulties in indexing content by making too many requests in a short period FAQ ✶ Want to know more? Learn the key features you need in an enterprise search tool Take Control of Your Data Maze with Enterprise Search Tools Empowering employees with a powerful enterprise search engine is a surefire way to boost productivity. Information is made easier to access, making decisions and forming strategies more straightforward. Say goodbye to time-consuming data navigation by adopting a tool like Unleash. An enterprise search engine doesn’t just deliver query results; it also delivers satisfaction and efficiency.

A drawing of a woman with a VR interface using personalized search

Enterprise search The Future of Search: Machine Learning and Personalization [2023]Machine learning, a subfield of computer science and artificial intelligence (AI), uses algorithms and data to mimic human learning, thereby improving accuracy over time. It's a widely-used technology for identifying patterns and predicting future occurrences based on historical data. As such, it helps people make sound decisions based on data-based insights. For example, it is often used to inform marketing decisions based on current market demand or to sort through large amounts of data and identify patterns in user behavior. Search engines like Google, Yahoo, and Bing continuously evolve to provide better user experiences, with machine learning significantly impacting search engine optimization (SEO) and personalization. Machine Learning in Search Engines Moz founder Rand Fishkin believes that the shift toward AI and machine learning in search will continue to lead toward more personalized search results that are better rooted in data and machine learning. Ultimately, an effective machine learning search engine can carry out several functions in the context of search engines. These include: Identifying relevant content automatically: By examining user behavior, machine learning identifies patterns and promotes matching content to deliver pertinent search items.Understanding user query intent: Machine learning can "understand" when a user wants to access information, buy something, or complete a task.Natural language processing: Machine learning can improve unclear queries or correct misspelled or inaccurately described search terms based on data and other users' behavior.Personalizing results: Machine learning personalizes content for users by capturing signals such as typical search queries and clicks, providing unique experiences through user recommendations.Detecting patterns: Search engines use machine learning to identify low-quality content such as spam and duplicates, weeding out irrelevant results and making the search process easier for users.Identifying new ranking signals: Google's machine learning algorithm helps identify potentially new ranking signals or markers that will help improve the quality of search results moving forward.Supplementing search technology: Machine learning isn't the only thing determining what comes out on SERPs (search engine results pages)—but it works well to supplement existing technologies and human developers' efforts to improve ranking signals and refine search.Image search: With machine learning, it's now possible to analyze and catalog hundreds of millions of photos uploaded to the web every day, allowing users to search for images using keywords or phrases.Synonym identification: Google's machine learning algorithm can identify synonyms, meaning that if a user queries "best running shoes" and another user has used the term "best running sneakers," Google will recognize both phrases as related. ✶ Find out how to speed up knowledge graph construction with ML Machine Learning in Google Search Google has always been at the forefront of developing search algorithms and still uses machine learning to optimize its search engine results. When a user starts typing something in the Google search box, for instance, the engine anticipates what they are looking for and instantly provides suggestions based on past searches, trends, or a user's current location. Google image search also uses machine learning to provide users with personalized experiences by presenting them with more relevant images. Did you know?Google introduced RankBrain, an AI, in 2015 and has since continued to deploy AI and machine learning to refine search processes, incorporating neural matching, BERT, and MUM. Google also ranks websites based on quality, protecting users from spam or fraudulent sites. Supercharge Your Search With Unleash The future of search will be increasingly influenced by machine learning. Search engines continuously strive to deliver more personalized experiences, with machine learning being crucial to achieving this. If you're looking to supercharge how you search for things within your organization, Unleash is a powerful search tool that uses machine learning to make searching effortless and efficient. Its simple, intuitive, easy-to-use interface can quickly search through every folder and application on your computer, helping you maximize your productivity.

A symbolic representation of NLP-boosted semantic search

Enterprise search How NLP Makes Semantic Search More Intuitive and Accurate Natural Language Processing, or NLP, is a branch of Artificial Intelligence designed to allow computers to process and analyze large amounts of natural language data. Simply, it allows computers to understand language like a human would. Now, with the help of machine learning technologies, NLP can convert natural language, with all its flaws, into a format that machines can comprehend. An organization that uses NLP for search queries sees enormous benefits. This is because a natural language processing search engine is designed to understand a searcher’s query and the context surrounding it. A semantic search, meanwhile, is understood as a search engine’s attempt to generate the most accurate results possible by understanding searcher intent, query context, and the relationship between words. With tasks that involve normalizing text and typo tolerance, NLP techniques can help make a semantic search more accurate in several ways. Normalization We know that language is complex. Across languages, each word has several variations, from tenses to superlatives to capitalizations. This is why we tend to add a lot of “randomness” when typing in search queries. Text normalization, one of the key techniques of NLP, is a process that attempts to translate and present these “random” keywords to a machine. Using NLP technologies, users can type in a query that’s not in the same format as the matching words and still find what they’re looking for. So creating a standard can help connect concepts and simplify language for a machine. Normalization also increases recall, which in this case is understood as a search engine’s attempts to find results that are known to be good. One example of how this works is letter normalization: In English, words are capitalized at the beginning of a sentence, whereas in German, all nouns are capitalized. While these rules are helpful for grammar, they make no difference in an information retrieval context, as the meanings of words don’t usually change when they are capitalized. So an NLP would essentially convert all letters to the same case, making it easier for a search engine to process results. Another common technique for NLP normalization is stemming, which reduces words to their root form. Yet another involves cutting large chunks of text into smaller pieces, called tokens. When we apply such normalization techniques to language, semantic searches become more effective since variations of words with the same meaning can be grouped together. ✶ Learn more about how enterprise search improves internal communication Typo Tolerance Typos can happen for several reasons. It could result from human error, a lack of language fluency, or simply hitting the wrong key. It could also result from a poor speech-to-text understanding tool. In the case of typos, finding the proper context matters the most, which is where NLP techniques can step in. NLP algorithms can help detect and correct spelling mistakes, making search queries easier to understand by a machine. But it's not just search queries that suffer from the issue of typos. When a document being searched for is made of user-generated content, it also runs the risk of having several typos. It's essential to fix this since if a search engine looks through a typo-heavy query, it may miss important information or struggle to provide accurate results. For this reason, NLP typo tolerance must be used across both queries and documents. Overall, typo tolerance is an essential feature of NLP technologies that helps improve the accuracy and usability of a semantic search. Entity Recognition Named Entity Recognition (NER) is another useful NLP technology that could help make semantic searches more efficient. A NER helps a machine identify all named entities and classify them into more refined categories, such as the name of a person, the name of an organization, and the name of the location, among others. By identifying key terms, or “entities,” in large amounts of text and grouping them, NER helps a semantic search to return more accurate results. Since NER can automatically tag documents, it can create an index of named entities at the time of ingestion rather than waiting until the search query has been submitted. This index, and moving the task from query time to ingestion time, helps improve the quality of search results. Besides that, NER is also valuable for determining intent. This involves going beyond understanding a specific query and determining the action a user wants to take when they type in the search. For example, when a user types in “blue skirt,” NER could trigger results related to the user’s actual purchase intent. Enhancing Semantic Search with NLP NLP is a powerful tool that significantly enhances semantic search capabilities. By analyzing and understanding the true meaning behind natural language, NLP enables computers to identify relevant content more accurately. Through the application of techniques such as normalization, typo tolerance, and entity recognition, NLP addresses the challenges of language complexity, spelling errors, and contextual understanding, resulting in more accurate and relevant search results. NLP greatly improves the accuracy and effectiveness of semantic searches, delivering more relevant results for even the most vaguely worded queries. By leveraging the capabilities of NLP, organizations can streamline their search experience and boost productivity. To explore these benefits firsthand, try Unleash—a user-friendly platform that offers GPT-powered answers to search questions. Sign up and try it for free today. ✶Sign up and try Unleash for free

Two pairs of hands exchanging an envelop and shaking hands from different locations, symbolizing the ease of internal communication with enterprise search