Enterprise information retrieval systems came into existence long before the public internet did. One of the earliest benefits to implementing multi-user mainframe computer systems was that they facilitated information discovery by finding exact matches to text strings in large document repositories.

With the growth of desktop computing and corporate intranets, commercial enterprise search solutions, such as the IBM Storage and Information Retrieval System (STAIRS) and the local search tool FAST (later acquired by Microsoft), became mainstream in enterprise computing.

However, the rise and popularization of free, publicly accessible web search engines, such as Google (and its predecessor AltaVista), radically transformed user expectations for information retrieval, content discovery and enterprise search platforms.

In the face of rapid growth in the volume and variety of data that enterprise search tools must examine, result retrieval speed has become a key indicator of cognitive search algorithm performance. Today’s intelligent search solutions must be built on architectures that can handle the performance demands of big data workloads. Because they deliver the necessary scalability, cloud infrastructures with extensive API-driven integrations and automation are usually best suited for the task.

Businesses can't use Google or other traditional search engines to find business-specific answers, such as "why is our new product shipment delayed?" or "what were our top reported customer challenges last week?" Intelligent search, unlike search engines and web search (such as Bing, Google Search, or AskJeeves), surfaces information and answers specific to your business.

Artificial Intelligence powers Intelligent search, equipping tools with the ability to:

• Understand human language. Business data is continuously updated and written in domain-specific terminology. Natural language processing capabilities enable intelligent search applications to understand and query digital content from multiple data sources. Semantic search and contextual understanding enable intelligent search to breakdown linguistic nuances, synonyms, and relations found in everyday language and inside complex documents.
• Learn document structure. Intelligent search tools (such as IBM Watson Discovery) have a document understanding AI that scales to understand many data sources. Machine learning enables intelligent search to learn the visual structure of documents specific to your enterprise, industry, or domain-space. With this understanding, intelligent search quickly learns and identifies elements such as headers, footers, charts, and tables. With out-of-the-box capabilities, it can recognize document types such as contracts, purchase orders, and invoices.
• Leverage machine learning. Machine learning and deep learning create seamless, immediate query suggestions and continuously improve search query result relevancy over time, predicting what information will provide the most value to users.
• Filter search results. Faceted and filtered search narrows the scope and finds specific information in data collections.
• Classify and categorize content. Entity extraction locates and classifies text data elements into predefined categories such as the names of persons, products, object type, or organizations.

• Connect data sources and ingest data: To pull answers and insights from anywhere, you need to connect and crawl all of your unstructured and structured data. A "connector" enables you to plug-in to a content source, such as Salesforce, Box, Microsoft SharePoint,  Databases, Web Crawler, or Uploaded data.
• Index content: Content indexing creates a single unified search index to allow for the homogenous ranking of search results, regardless of their source.
• Enrich content: The ability to query and extract insights is dependent on the ability to extract metadata from your content. Enrich your content by leveraging out of the box natural language processing enrichments, such as entity extraction and sentiment analysis, to categorize and identify key content.
• Analyze content: Recognizes the contents of documents, classifies them, and creates the semantic correlations between the individual pieces of content.
• Delivers answers and displays insights: Intelligent scoring algorithms rank passages, provide users with the most accurate, relevant passages and snippets in response to the query.

Intelligent search compares terms within natural language queries against the content in its indexed information.

• Questions - "how many vacation days can I take at work?"
• Phrases and commands - "I am applying for a mortgage."
• Keywords - "insurance rates."

• Discovers insights to drive decisions: There are insights hidden in your unstructured text data. Intelligent search applications use natural language processing to discern meaning and make correlations across data sources – such as social media (tweets, LinkedIn), customer feedback, e-commerce reports, and maintenance records - to reveal real-time insights with speed and precision.
• Puts relevant information at your employees' fingertips: Use intelligent search to create an enterprise search platform, knowledge management, content management system, or question answering system to provide a simple team-wide user experience.
• Provides customer service at scale: Give your customers the right answer every time and provide a better customer experience. Customers want more than FAQs. Now more than ever, they want to fully self-serve on your websites and mobile applications — virtual agents and intelligent search allow your customers to achieve independence. Self-sufficient customers translate to reduced support costs and higher customer satisfaction.

Businesses store documents and data across multiple sources in unstructured and structured forms. On average, employees waste 3 hours each workday searching for information.

Finding insights and answers in your company’s unstructured data should be easy. It's time for your business to become data-driven with intelligent search.

• Saves time. Banks were able to automate knowledge discovery to complete ten days worth of work in two minutes. Learn more
• Saves money. An energy client has saved upwards of USD 10 million worth of time by cutting down on time spent searching for relevant information inside their enterprise knowledge bases. Learn more
• Reduces workload. An insurance client reduced reading and analyzing of internal enterprise data workload by 90%. Learn more 
• Drives revenue. Law firms use search applications to improve business processes and become four times more productive, generating revenue increases of as much as 30%. Learn more