The Swiftype Blog / Tag: search technology

Introducing the Swiftype Search Sallet

Have you ever found yourself on one of your favorite websites, hunting for something very specific, typing query after query into the search field, thinking to yourself: “I wish my computer could just read my mind and find what I’m looking for!”

Well, great news, search fans! Swiftype, the largest independent Site Search and Enterprise Search provider on the web, has solved this long-standing, life-menacing problem just for you. If you ever thought hands-free site search was just a distant thing of the future, you’re wrong! The future is now and now is the future. Today, Swiftype is revealing a cutting-edge site search helmet designed to find what you’re looking for before you even know you’re looking for it: Introducing the Swiftype Search Sallet.

Powering Site Search While Searching Your Mind

We’ve extended our platform to include our patented Neuro-Learning Behavior Recognition technology (NLBR) – you think it, we search it! Get started with the Swiftype Search Sallet in 3 easy steps:

  • Step 1: Wearing Swiftype’s Search Sallet, stare at your computer’s search box for 3 seconds.
  • Step 2: Using Swiftype’s AI, NLP, ML and NLBR technologies, our search automation will recognize that you were searching for, (ie: “Do elephants have elbows?”) and auto-populate your query and immediately trigger the search.
  • Step 3: Review the set of results and stare for 3 seconds at the source you’d like to see more of – additional similar content will automatically appear! Voila! Search that reads your mind.

Unbelievable, Way-Too-Good-To-Be-True Features!

Standard Features include:

Split-Second Search: Our NLBR technology gets smarter over time by learning your eye patterns, brain activity and queries so your search results are returned to you even faster.

Productivity Filtering: Got a deadline coming up, but can’t stop wondering who would win in a fight between Iron Man and Batman? The Search Sallet knows you have to finish your work and won’t show you those results until after you get your work done.

Vocabulary enhancement: Still not sure if it’s “your” or “you’re?” The Swiftype Search Sallet knows which one you actually meant.

Available Enhanced Features:

Song Recognition: Ever get the melody of a song stuck in your head, with no way to figure out what it is? If you’re wearing the Swiftype Search Sallet, our algorithm will pick out the tune and find you that song!

Hunger Recognition: Craving Thai food?  Swiftype will find the restaurant you want in milliseconds before you even know you’re hungry.

Additional Sallet Specs:

  • Breathable, moisture-wicking liner, removable and washable – since you’ll surely work up a sweat -while you feverishly query
  • Color: Flat Black and rubber finish making it the ideal accessory, even for a night out on the town!
  • Incredibly lightweight! Only 10.5 lbs
  • 5-year warranty
  • Batteries not included

Our Customers Have Amazing Things to Say

(Statements given totally and completely by their own free will!)

“Before the Swiftype Search Sallet, I couldn’t find anything I really needed on the internet. Now, I can get information from any website just by thinking it!” – Dave, International Geographic

“I can’t believe I spent all these years wasting time using my hands to search!” – Mindy, Buzzton Post

Dozens of satisfied customers agree – Swiftype’s Search Sallet has not only revolutionized their modern search querying habits, but has made them more likable, attractive, and generally successful. Don’t wait. Order yours today!    

Just kidding. Early April Fool’s! But real talk, our search is pretty damn good. Try it for yourself.

Essential Site Search Terms You Should Know

When evaluating your next Site Search solution, you may come across some terms that you’re not familiar with. We are often asked, What does this mean? From Bigram Matching, to Corpus, to Autocomplete, having a grasp of these common terms will help you better understand and evaluate your next website search engine platform. This post provides you with explanations for the most popular/common site search terms.

Essential Site Search Terms You Should Know

Autocomplete
Autocomplete (also known as typeahead or autosuggest) is a language prediction tool that many search interfaces use to provide suggestions for users as they type in a query.

Bigram matching
Bigram matching is a language analysis tool which advanced search engines use to find results for multiple-word queries that are similar to but not exactly the same as the text in the searchable index. (example: iPhone, i phone, i-phone are all different terms, but the algorithm recognizes them as the same)

Clickthrough behavior
Clickthrough behavior is a type of data that records what results users are clicking on from an SERP.

Constant Crawl
Constant Crawl is a Swiftype feature that allows your search engine index to be updated in real time. This means that any time a page is created or updated, Swiftbot will immediately index the new content and make it available in search results.

Conversions/Conversion rates
In the world of search, conversions occur when users perform a query and click on a result from the SERP.

Corpus
The corpus is the entire body of searchable text in a search index.

Document
The term document refers to a single page or item in a website search index. On a publishing website, for example, a document could be a single article. On an ecommerce website, a document could be a product listing.

Document frequency
Document frequency is the number of times a given term or query appears in a specific document within a larger search index.

Exit rate
Exit rate is a measurement in search analytics that records how often users perform a query then leave the SERP without clicking on a result.

Filtered search
Filtering is a search tool that lets users to restrict their search to a certain section of a website or a specific document type.

Phrase matching
Phrase matching is a language-dependent process which advanced search engines use to identify sets of words that should be treated as a cohesive unit when scanning across a search index for the most relevant documents.

Search query
Queries are the strings of text that users type into a search bar when looking for a specific result or set of results. Queries can be one word or several.

Stemming
Stemming is a language-dependent process of removing suffixes from words so that words with the same root match each other.

Term frequency
Term frequency is the number of times a given term or query apears within a search index.

To see the complete list of keywords, visit our Search Concepts overview page.

Your Search for Love Love for Search Starts Here

On Valentine’s Day, it’s easy to be bitter if you can’t find what you’re looking for.

Searching for data over a stack of different cloud technologies (Salesforce, Dropbox, G Suite, etc) is like hitting the bars every weekend, hoping your soulmate walks up beside you and asks, what’s your sign?

Sure, you might get lucky, but chances are at the end of the night you’ll be left sifting through irrelevant results and looking for your match in all the wrong places.

We just launched the world’s best wingman for your cloud technology suite. Using AI technologies, Swiftype Enterprise Search understands context — we get that it’s not just about what you say, but how you say it.

We’ve married artificial intelligence with our industry-leading algorithm, to make indexing content across disparate cloud data sources effortless. Employees get the information they need, when and where they need it.

Stop settling. Get Swiftype Enterprise Search and fall in love with your cloud technologies again.

Teaching Swiftbot to Intelligently Index Images

When creating search engines, the first and arguably most important step is indexing website information in a structured format that is optimized for a specific search algorithm. The specific information you index and the structure by which you organize this information (also known as the schema) dictates how your search engine will determine relevance, what your users can search by, and what information you can display in search results.

How does indexing work?
While there are numerous ways to customize and control the information you index in your Swiftype search engine (for example, via our API or one of our platform integrations) we aim to make this process as simple as possible for non-technical users by automatically indexing website information with Swiftbot—our high performance web crawler designed to index information from a specific URL.

Swiftbot allows non-technical users to get up and running with a working search engine in minutes by simply entering their website URL and letting Swiftbot index their website for them. A major component of Swiftbot’s technology is the logic that our engineering team has built in to parse website HTML and index it in a structured format that works with Swiftype’s advanced search algorithm and information retrieval method. (To learn more about the technical challenge of building a search engine, read our white paper on the subject, written for a non-technical audience).

Building an intelligent web crawler
Because almost every website is built and structured in a different way, teaching Swiftbot how to effectively read, sort, and organize information from a website’s HTML base is an ongoing challenge. While we do allow site owners to completely customize the default information Swiftbot indexes from your website with custom <meta> tags, not all users have the technical resources or knowledge to do this on their own, so Swiftbot is also built to make many of these indexing decisions on its own.

HTML windows

With every website structured differently, how do we teach Swiftbot to intelligently index this information?

Still, with websites differing so dramatically from one another, indexing the right information in the right format from each page is no easy task. In particular, identifying the most important image from a web page and associating that image with a search result is a multifaceted problem, since there are many images on every page and these images often have different filename structures and/or occupy different locations on a page.

images in search and autocomplete

Adding images to search results pages and autocomplete menus can create a much more engaging search experience.

Nevertheless, indexing images allows site owners to create much more engaging search experience, adding thumbnails of varying sizes to their autocomplete and search results that let users see a preview of the page content before selecting a result. So, in a recent update to Swiftbot, we’ve built in conditional logic that automatically indexes images from your website pages (provided there are no Swiftype specific image tags already in place).

How does Swiftbot decide which image is “best”?
To teach Swiftbot how to index the “best” image from web pages, we had to build in logic that would overcome a series of challenges that result from the varying nature of website pages.

  1. As a starting point, we decided to leverage existing open graph <meta> tags (such as Facebook and Twitter <meta> tags) that many site owners use to prepare their content for sharing on social media platforms and other content distribution networks. By teaching Swiftbot to obey these <meta> tags if no Swiftype specific <meta> tags exist, we created hierarchical indexing logic that more intelligently sources images from existing website metadata.
  2. Secondly, we know that many websites have a large number of images that repeat across many, if not every page on their website (for example: a company logo, images in the header, footer, and sidebar, author headshots, ads, etc.). To ensure these images are not considered the “best” image for a specific document, we built in logic that identifies and rules out these repeating elements as candidates. Similarly, we do not want to index advertisements, so we run any images on the page against an ad server blacklist to ensure these remain out of consideration.
  3. Thirdly, we compared data in the alt attribute of each <img> with the url and <title> of that page, assigning a relevance score to those images based on how closely the alt description matched this page information.
  4. Lastly, Swiftbot looks for common CSS classes and id’s to locate the main content area of each page—another step that helps rule out extraneous information such as the header, footer, and sidebar.

Taking all these pieces of information together, Swiftbot assigns the images on the page a relevance score and indexes the image it judges to be the “best” image for that document. As this new indexing process gains wider use and we gather feedback from customers, we will continually work to improve our image extraction technology over time.

Adding these images to search
Once these images are indexed from your website and in your search engine, the question becomes: how do I display these image thumbnails in my search results and autocomplete dropdown? While there are many ways to style your autocomplete and search results (including using Swiftype’s web components or jQuery library) the best choice for users with very little technical experience is the Result Designer, which allows users to style their search results entirely from the Swiftype dashboard without writing any additional code. To learn more about the Result Designer, watch our dedicated webinar explaining this tool and offering best practices advice from the Swiftype customer success team.