A computer once beat me at chess, but it was no match for me at kick boxing. —Emo Philips

Ever wondered if (more like when) computers will get the bright idea to boot out their human overlords and take over?

You’re not alone. Artificial intelligence is powering more of humanity’s technology, and computers and software are “studying” and getting smarter every day. 

The domain of search is one area where smart technology is certainly taking over. AI-powered search tools are now being used in applications ranging from corporate data management to ecommerce shopping experiences. 

For instance, you’ve probably noticed lately how smart Google has gotten at giving you relevant information from the Web. That’s because it’s an intelligent search engine, in fact the smartest one on the planet. But smaller-scale, enterprise search-engine versions tailored for corporate knowledge bases and other use cases aren’t far behind in adopting this expanding functionality.

What is intelligent search?

Intelligent search capabilities are what you get from a search engine that has the ability to “understand” user intent based on what the person enters in the search box. Also known as cognitive search and AI search, it utilizes a range of technologies, such as natural language processing (NLP) and machine learning, to better understand what each user wants. Once that’s identified, the right search query results can be provided.

That’s pretty amazing when you realize that it wasn’t long ago when the only kind of search available was the “dumb” (for lack of a better word) kind, now called “traditional search.”

What’s an example of traditional search?

A few years ago, you might have done a text search on a website that used a traditional search engine; maybe you entered something like “vegan ice cream.” You would have been presented with the highest-ranking pages for the term, basically a list of links to products on websites of various grocery stores that sold vegan brands. 

If you were looking for basic information on where you could grab a gallon of vegan ice cream, this search method could have worked fine. But you may have been looking for something more specific related to vegan ice cream. 

This is where modern intelligent search solutions come in more handy.

What’s an example of intelligent search?

If you type “vegan ice cream” in the search box in Google, your experience will be considerably more impressive. AI-powered intelligent engines still use web crawlers and rank web pages, but the software also analyzes user intent.

When you search on a broad-based term like this, an intelligent search application groups results in intent-based categories, like shopping options, recipes, and answers to frequently asked questions. Then you can quickly zero in on the category you had in mind and drill down in the results.

“Vegan ice cream” covers a lot. Rather than know where you can buy it, you might just want to find out what determines authentic vegan ice cream, or what ingredients are in it.

Intelligent AI-powered search engine technology considers all of these variables, so you’re less likely to have to repeat a search query because the first one fails. The search results are typically more accurate and naturally line up with your specific desires, whether you’re having a vegan ice cream craving or just want to know how it differs from vegetarian ice cream (is there such a thing?). And just in case you haven’t quite articulated which exact vegan-ice-cream data you’re looking for, you’ll be shown “People also ask” questions, such as “Is vegan ice cream healthier than normal ice cream?”

How does intelligent search work?

So now we know that intelligent search is based on discerning a searcher’s specific intentions in order to quickly supply the right information. But how does the intelligent search information-deciphering process actually work? 

Intelligent search combines techniques including natural language processing and machine learning. For example, it can make connections between semantic terms that a traditional search engine (one that’s simply looking at keywords) would be unable to discern. It can also do “thinking” types of things like understand the structure of a document.

Let’s look at these capabilities in a little detail.

Filtering content 

Web crawlers slog through a ton of structured and unstructured data to identify the most accurate search results. Because of this unwieldy chore, intelligent search engines employ advanced filters that narrow the scope of searches in order to home in on what’s most relevant.

Analyzing document structure

Intelligent search is also a help in content-management activity like locating PDFs, templates, and visual one-pagers. That’s because it uses document-scanning technology that can identify the type and structure of a document, for example, whether it’s an infographic, a contract, or an invoice; whether it contains tables, a table of contents, headers, or footers. The software might take an “educated guess” about which document format the searcher is seeking, and, based on that, prioritize the most relevant items for the searcher.

Applying natural language processing 

Natural language processing is the ability of a computer program to understand spoken and written human language. NLP enables search engines to understand digital content pulled from multiple data sources and interpret abstract linguistic structures, such as those containing synonyms, nuances, or complex relationships between words. A search application that can learn the meaning behind people’s language can then more effectively predict search intent.

Applying machine learning 

Machine learning is a branch of artificial intelligence that uses data and algorithms to replicate the way humans learn. The learning happens in response to what’s being detected.

For example, if someone has been searching for a particular topic and then starts a different search for something seemingly related, the machine learning functionality could assume that the two searches are indeed related and proactively suggest material that encompasses both topics. With each additional query, over time, the search engine gets better (smarter) at predicting the most accurate search results. 

Another application of machine learning is when an intelligent search engine detects information patterns, which can then aid it in identifying spam and duplicated content. That means the searcher doesn’t have to be subjected to digital reams of unwanted information.

Make an intelligent choice for your site

Is your company still using traditional search technology for your website or apps? Now that you’ve read this post, you may be wondering whether you should optimize by adding intelligent search. 

If that idea makes sense, check out Algolia’s fully hosted SaaS search solution. We provide AI-powered intelligent search that lets users easily find what they want, discover new items of interest along the way, and emerge fully satisfied from the excellent customer experience they just enjoyed. 

From a development angle, whether your site is focused on ecommerce, health care, media, or some other sector, our robust API will let you create and streamline the search experience to meet the needs of both your users and your team. Contact us to get details, or start building great enterprise content search functionality now for free.