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What is Intelligent Search?

Learn the definition and history of intelligent search, an artificial intelligence powered search experience that returns better search results.

Young child looks to the sky through a homemade telescope while wearing a wizard cape and hat

Intelligent search uses artificial intelligence to provide users with a natural language-like interaction in the context of a digital search query. Intelligent search systems are able to understand the intent and meaning of queries, and respond to them in a way that feels more natural than standard search. It’s the difference between a frustrating experience and one that feels almost magical.

In a standard search experience, a user types a question or query into a search engine and is quickly presented with results that tie back to the keywords used in their query. This is especially true for things like on-site search, where a user is searching a repository of website content. The technology powering this type of standard search is often limited. Someone searching for “event in May” would likely assume a match for results like, “This event may include…” which is unlikely to be what the searcher is looking for.

For search engines like Google and Bing, there’s significantly more technology that goes into understanding the query and presenting relevant results. That same query, “event in May” entered into Google is likely to bring forth actual events happening in the month of May. The difference? Intelligent search technology, which is actually a combination of technologies that come together to power improved search experiences. 

Intelligent search technology can access and learn from multiple different formats of information, adds context beyond the written word, and delivers results in near real-time. Intelligent search applies machine learning and AI at scale to return results based on an understanding of context, not just keywords.

Intelligent search systems are able to understand the intent and meaning of queries, and respond to queries in a way that is more natural than standard search.

This means they can provide results that are more accurate, relevant and useful.

For example, if you search for “car” on an intelligent search engine like Google or Bing it will look at your query and return results based on the context of what you’re looking for. It won’t just return pages about cars; it will also give you information about places where you can buy them, with links to dealership locations and for sale listings, reviews about cars from other websites (such as Consumer Reports), articles written by experts in the field related to cars, and more.

This means that you can use your browser to find something specific quickly: “Show me pictures of cute pandas.” Or you can ask broader questions: “How many pandas are there?” The answers returned will pull from a variety of sources ranked as trustworthy and high value, and some search engines will even identify the most likely response and offer it up in a way that feels conversational.

Screenshot of a Google search query asking,

Intelligent search systems use machine learning and AI at scale to return results based on this understanding of context, not just keywords.

Machine learning also allows intelligent search systems to adapt over time, which means the more you interact with them, the better they understand what you want—and it gets smarter as it goes along. This is especially useful when considering that a lot of content on the web isn’t well-defined or tagged, and much of it is visual.

Intelligent search systems also use artificial intelligence (AI) in order to process user intent and identify relevant contexts around specific questions or tasks so they can provide a meaningful response back to users. This requires an investment and understanding of linguistics, the study of language, because words have meaning only within certain contexts; these tools must be able to understand those nuances in order for users’ queries into something useful without returning unrelated results, like the “events in May” example mentioned above.

Indexing Content and Building Knowledge Graphs to Support Intelligent Search

It’s important to understand that the work to present highly relevant search engine results actually starts long before a user starts to type their search query. 

Search engines have bots that crawl and index pages of content online. This site indexing is something that website owners proactively request to ensure that their content is found. 

Once the content has been found and indexed, search engines maintain their own indexes and complex algorithms that rank and return results for individual queries, arranged hierarchically to show the ones most likely to be relevant higher up in the list. Over the years, this has caused many users to avoid looking beyond the first page or two of search results. 

Because of this, companies spend billions of dollars each year on Search Engine Optimization to ensure that their content can be found, indexed, and ranked high enough to show up at the top of the hierarchy. Despite this investment in optimizing their content for search, 44% of site visitors are still unable to find the information that they’re looking for on a website, even when the information is there. This leads to significant drop-offs and negatively impacts customers and businesses alike. 

As technology continues to improve and become more accessible, features like artificial intelligence powered intelligent search offer ways for companies to improve their on-site experiences in a way that directly benefits the 44% of site visitors who can’t find the information that they need. 

The technology used by search engines, and designed to index the entirety of the internet, can also be applied to specific websites, apps, and more.

These intelligent search systems have extensive knowledge graphs that give them context about their queries and the world in general. In a smaller “world” of information, like a company website, intelligent search technology can be applied specifically to the company’s knowledge graph, to return contextually relevant responses specific to the company information.

At XAPP AI, we call this the Unified Knowledge Model™ and it’s designed to return highly relevant and accurate answers to specific questions like “when are you open?” and “how much does a bathroom renovation cost?” 

A website leveraging the Unified Knowledge Model™ can provide responses that include specific numbers based on square footage, sample projects, or even a link to a project cost estimator. 

Intelligent search makes it easy to surface the answer or a way to easily find the answer, quickly. 

It’s estimated that on Google alone there are over 5.6 billion searches every single day. Because of the proliferation of intelligent search experiences like this, consumer expectations around what a “good” search experience is have rapidly changed. 

It’s no longer accepted that people do the manual work of searching for answers. They want AI to do it for them. This expectation has shifted beyond search engines and every company should be looking at ways to add intelligent search to their on-site experience.

Looking for more ways to improve your on-site search? Read this: Three Tips to Supercharge On-Site Search.

Contact us to discuss how we can help any size business launch Conversational AI experiences that people want to use.