Algolia, the world's only end-to-end AI Search and Discovery platform, today announced that Everlane, a retail clothing company that promotes sustainability and transparency at every level, selected and is using Algolia NeuralSearch™.

Everlane Boosts Conversions and Slashes Null Search Results with Blazing Fast Algolia

These three trends reflect the evolving landscape of consumer behavior. Technology and inflation’s combined impact on back-to-school shopping, a pragmatic Prime Day, and the women's multiplier effect on consumer behavior offer valuable insights for businesses seeking to adapt and thrive in a constantly changing market.

The Ripple Effect of Cultural Events on Consumer Behavior

Amplience, the AI Content company, today announced that Shoe Carnival, one of America's largest family footwear retailers, has increased conversions and revenue, enhanced campaign efficiency, improved customer experience,

Shoe Carnival Achieves Higher Conversion Rate and Delivers Lightning-Fast Campaigns with Amplience and Algolia

Well, now you can use generative AI tools along with Algolia NeuralSearch to bring your idea to life. Here’s a step-by-step example: Start with using ChatGPT to conduct a business brainstorming ...

How to use AI to build your business

Let's dive into a use case that showcases how Segment, Mixpanel, and Algolia can be seamlessly integrated to drive insights and offer personalized content recommendations for a travel site.

Recipes for Success: Personalizing Travel Experiences with Segment, Mixpanel, and Algolia

Algolia, the world’s only end-to-end AI Search and Discovery platform, was selected by Revival Animal Health, a national pet health supply company, to significantly improve revenue conversions ...

Algolia Boosts Revival Animal Health’s Revenue Conversion by 12% with AI-Powered Search

Get the most recent info and news about Algolia on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Our story began in 2012 with the idea of creating blazing fast, instant and relevant search and discovery experiences. In essence, we reduced the need for online visitors and in-app users (using any web, mobile or voice device) to needlessly forage for information — by surfacing the desired information in as short a time as possible. 

Millions of developers around the world use Algolia to unleash the power of search across their digital properties. They use Algolia because we are API-first. We unlock incredible flexibility. We build blazing fast speed into our APIs. We do this because we know developers want to build cool things and innovate. 

Under Armour, Lacoste, Birchbox, Stripe, Slack, Medium, Zendesk and 10,000+ other companies use Algolia to build not just great search, but search that converts at higher rates because of the relevancy. We help their customers find the right goods, offers, and content really quickly.  So quickly, in fact, that they don’t go anywhere else.

Algolia helps businesses build and optimize the search and discovery experience resulting in significantly enhanced online engagement, increased conversion rates and enriched lifetime value that generates profitable growth.

Today, Algolia powers 1.5 Trillion searches a year – that’s 4 times more than Bing, Yahoo, DuckDuckGo, Baidu and Yandex combined. Our amazing technology is used by one in eight online users. 

We’re looking for talented, passionate people. As an ownership-driven company, we seek team members who thrive within an environment based on autonomy and diversity. 

We're committed to building an inclusive and diverse workplace. We care about each other and the world around us, and embrace talented people regardless of their race, age, ancestry, religion, sex, gender identity, sexual orientation, marital status, color, veteran status, disability or socioeconomic background.

Algolia

While I was at Algolia’s Paris office last week, Dhaya Benmessaoud from our Front-end Experience team showed me a cool trick for styling Algolia’s React widgets in your UI. Out-of-the-box, Algolia provides a couple of pre-built themes for search experiences (Algolia and Satellite) as well as the ability to create custom themes. Recently, the front-end experience team has added a third way to style your UI, by into your Algolia injecting custom CSS classes React components. This is great news for people who use class-based like Bootstrap and ! In my case, I was working with the Algolia , which relies on Tailwind for styling. I wanted to add a widget from the Algolia Recommend UI library to my homepage, but I wanted to style it using Tailwind classes to match the rest of my front-end. CSS frameworks Tailwind Ecommerce UI Template <TrendingFacets> Here’s how it looks before styling: I can definitely do better! To accomplish this, I need to use the attribute for my component. It’s available for all of Algolia’s React widgets (including Recommend) and it allows you to override styling on component-specific elements. Some of our other front-end APIs like Vanilla JavaScript ( ) and Vue ( ) have had this functionality for years, and now it’s available in React thanks to the recent refresh that added React hooks. classNames cssClasses class-names The documentation lists the elements I can override for each Algolia widget. For instance, here are the elements of a widget: <SearchBox> : The root element of the widget. root : The form element. form : The input element. input : The submit button. submit : The reset button. reset : The loading indicator element. loadingIndicator : The submit icon. submitIcon : The reset icon. resetIcon : The loading icon. loadingIcon For my widget, I want the list in a horizontal line to conserve space, so I add a class to its element. I’ll also add a new class to give each a nice capsule shape with some simple hover styling. Here’s how my component looks after styling. <TrendingFacets> flex list facetItem item And here’s the code: <TrendingFacets
  classNames={{ list: 'flex', item: 'facetItem' }}
  recommendClient={recommendClient}
  indexName={indexName}
  maxRecommendations={3}
  itemComponent={({ item }) => (
    <a href={item.facetValue}>{item.facetValue}</a>
  )}
  facetName={facetName}
/> Adding to customize the style of Algolia widgets makes so much sense, especially for people like me who are addicted to Tailwind CSS for styling front-ends. You can read more about adding custom CSS classes to widgets in the . If you’re new to Algolia, you can try it out by signing up for a . classNames Algolia documentation free tier account Also published here.

Using Tailwind Classes to Customize Algolia React Components

Streamlining Data Debugging for Game Engine Development with Algolia and Rust

I teamed up with Starschema Full Stack Engineer Soma Osvay to write about a project near and dear to my heart: developer documentation. Soma and I both hope you enjoy this article where we cover the use-case and challenges and the implementation Soma came up with. Here at Starschema, we have a lot of projects we’ve done as part of a consultation with . When we’re supporting these existing solutions or wanting to develop a new project, we often want to search through all of the documentation. We currently don’t have a solution, costing us man-hours since we have to do this work manually. markdown documentation Use Case We need to be able to search for projects related to specific topics to check implementation specifics, get ideas from the code, and much more. The Sales team also needs a solution to determine what kind of projects our team has completed for certain topics and be able to quickly communicate it back to potential clients. It would also be great to have something like an internal Stack Overflow for our Developers since we are doing a lot of deep technical work in Tableau. Project Managers also need to be able to determine which employees have worked with certain technologies so they can get questions answered quickly and easily. In short, we need to be able to search for our own projects based on the following attributes: Technologies used (coding languages, databases, etc) Project keywords (tags) Project documentation itself such as install instructions, technical documentation, etc. Timeframe of when the project occurred All of these attributes exist within the internal documentation markdown files – we just need a way to search them. Implementation Plan The plan is to use a as a proof of concept which will: NodeJS CLI Scrape the top GitHub public repositories and grabs the README markdown file (which will ultimately represent our internal projects) Stores the documentation files into Algolia alongside the project’s basic information (title, programming language(s), tags, etc) The CLI will contain advanced logging and command line arguments for ease of use. We also want to host it on the web so people can try it out. Challenge The biggest challenge at hand is the record size — Algolia only allows our records to be 100KB at the most. However, most markdown documentation files are much larger. The solution is that we need to split up the markdown files into multiple pieces within the Index. We also then need to make sure that when we search for something, a single project only appears once—even though it is split into multiple records. Fortunately, Algolia has a distinct feature so we can de-duplicate these results very easily. Indexer Implementation To make using the Indexer as easy as possible, I opted to create a CLI for it as mentioned above. After supplying the arguments required, the tool will automatically initialize the repository, remove any existing records, and configure the relevancy settings. Powering the tool is a straightforward GitHub API that grabs the requested number of top repositories and extracts all of their metadata and downloads the README file. It’ll also filter out repositories that are missing an owner or missing a README file, giving us the best results. Finally, it’ll also convert the markdown content to HTML for easier rendering in the frontend. To keep the record size in check, the tool will automatically split up READMEs over 50k characters into additional records. This way the records won’t get too large but one record should still serve almost all repositories. It’ll then sync this information over to Algolia 100 records at a time so we can meet their batching recommendations as described in the documentation. here Frontend Implementation To serve as my starting point, I took advantage of the library released by Algolia and launched an InstantSearch.js boilerplate. From here, I was able to add additional widgets provided by InstantSearch.js such as the pagination and the page size selector which worked great. create-instantsearch-app As we also collected repository metadata with the markdown, we also needed to customize the hits component to include this additional information. Often times the metadata is as important as the library description so developers can at a glance see if it’s a popular library, who released it, the tags, and much more. I also added facets so Users could filter by the programming language, tags, or how many times it has been forked. The final piece to the puzzle was adding the ‘Open documentation’ button allowing you to quickly and easily read the markdown content for the repository in a pop-up without leaving the application. If the record we are clicking on has multiple rows, it’ll automatically load the additional records and concatenate them in the display—awesome! Conclusion This project was a fun test and really showed me how flexible Algolia is for different use cases such as ours. The ready-to-go widgets saved me a lot of time during prototyping and having relevant results from the first few keystrokes is super impressive. I also think it would be super interesting if we were able to harness the power of Algolia Recommend if we were able to generate enough events from Users clicking on projects internally. You can view a live demo of the GitHub test project , with a button that will set you up with the default demo credentials to view our index. Interested in the backend indexing code? You can find that on GitHub! here here Also published here.

Our Journey to Building a Searchable Index with Algolia and NodeJS

Nuestro viaje para crear un índice de búsqueda con Algolia y NodeJS

Notre parcours vers la création d'un index consultable avec Algolia et NodeJS

Algolia और NodeJS के साथ एक खोज योग्य इंडेक्स बनाने की हमारी यात्रा

Algolia と NodeJS を使用して検索可能なインデックスを構築するまでの道のり

Nossa jornada para construir um índice pesquisável com Algolia e NodeJS

Hành trình xây dựng chỉ mục có thể tìm kiếm của chúng tôi với Algolia và NodeJS

我们使用 Algolia 和 NodeJS 构建可搜索索引的旅程

Ingesting data should be easy, but all too often, it can be anything but. Data can come in many different formats, from many different sources, and wrangling it all can be a time-consuming and frustrating process. At Algolia, we believe that connecting data should be simple, and we’re committed to providing our customers with the tools they need to make that a reality. That’s why we’re excited to announce our new no-code connector platform with new and connectors! JSON CSV With this new platform, you can quickly and easily connect to the data you need, without having to worry about complex configurations or coding skills. Simply tell us where your data is located, how you want to ingest it, and where you want it to go, and we’ll handle the rest. Whether you’re a data analyst, a business user, or a developer, our new no-code data connector platform can help simplify getting data into Algolia without any headaches or hassles. Built for popular data types Since JSON and CSV files are some of the most ubiquitous formats for data out there, we wanted some of our first few connectors to be focused on these file types. Whether you’re working with homegrown business processes, exports from your content management system, or just plain hosted files, our connectors can help you quickly and easily access the data you need. Each connector comes with an easy interface to set up as well as a matching API that allows you to fetch data from JSON and CSVs either on-demand or on a schedule. This means that you can spend less time struggling to connect to your data, and more time focusing on building amazing search and discovery experiences. Ready to get started with our new no-code JSON and CSV connectors? It’s easy! In just a few simple steps, you can bring the data you need into your Algolia index. Follow along with this quick guide to setting up our JSON connector with a sample file of movie data: https://raw.githubusercontent.com/algolia/datasets/master/movies/records.json All you will need for this guide are the above URL and an Algolia account. Your account must have permission to create and manage API keys within the app you are using. Before you begin, make sure you have an API key that you can use with the connector. The API key must have the addObject, deleteIndex, and editSettings permissions and will be used to move data into your indices. To set up any connector, whether it’s JSON, CSV, or otherwise, there are three parts to configure: : Configure the connector by indicating where your data is located, if there is any authentication needed, and how to identify a unique record. The data source : Let us know which index you want to send this data to. The destination : How and when do you want to fetch the data? On-demand or on a schedule? The task Let’s walk through each part step-by-step. Configure your data source First, we need to create a new data source based on the movie data URL. Visit the in the Algolia dashboard to find our no-code connectors along with a variety of ways to connect your data. Connectors page Click the Connect button for the JSON connector. Then select Get Started. Let’s create a new source by first selecting the appropriate authentication type. Since our JSON file is fully open to the public we’ll select None as our authentication type. The JSON and CSV connectors currently support publicly available files and those protected with a username and password. Next, enter the URL listed above as our source of data. Our connectors support both HTTP and FTP-hosted files. Now we need to configure the property that uniquely identifies a record in our data. This will often be some kind of ID, SKU, or similar unique field. In the sample movie data set, we already have a property called objectID that acts as the unique identifier so we will enter “objectID”. Whatever property you set as your unique identifier will be used as the objectID in your index. Enter a name to identify this data source. This name should be easily recognizable and human-readable so you can reuse it in other areas of the connector platform. Click Create Source to create a new data source and move to the next step. Configure your destination Next, we need to create a new destination that points at your index. When sending data to your index we will use one of your own API keys to authenticate. Select a specific API key to use with this connector by first clicking on the “Search or create an authentication” input and then selecting “Create a new Algolia authentication”. Here you can choose which existing API key to use with the connector. The API key must have the following permissions: addObject, deleteIndex, and editSettings. If you do not see an eligible key listed, you will need to first make a new one on the page in the Algolia dashboard. API key settings Once you’ve created an authentication, select an existing index or enter a specific name to create a new one. Enter a name to identify this destination. Again, make sure it’s something easily identifiable so you can reuse it in other areas of the connector platform. Click Create Destination to create a new destination and move to the final step. Configure your task A task is a way to define how and when your data will be fetched and ingested. For this guide, we’ll set up the connector to fetch data on a daily basis. Select the Scheduled option to show the frequency input. Choose an option from the drop-down menu’s list of popular schedules or enter a cron expression. To have Algolia fetch and update the data in our index daily we can simply select Every day. Click Create task to create a new task. And that’s it! You’ve now set up a connector and your data will be pushed into your index every day automatically! If you want to see it in action now, simply click the run button with the play icon to kick off your data task right now. If you created a new index in the setup process, you will now see it available with all the movie data! We’re committed to providing you with a powerful and flexible connectivity platform so you can focus on building robust search and discovery experiences. We plan to continuously expand our no-code connector platform to include even more data sources and more data processing capabilities. Whether you’re working with CMS data, an e-commerce platform, or databases, we want to make it easy for you to connect to the data you need and be confident in your data ingestion pipeline. Visit the new page in your dashboard to see our new no-code data connectors and let us know what new connectors you want us to build next! Connectors Algolia Also published . here

Effortlessly Connect Data with Algolia's New No-Code Platform

falls on March 11th in the United States to encourage more people to build things online without code. To celebrate this day, we challenged English teacher Alita Leite de Silva (who is not a developer) to build something cool using Algolia’s API-first search tools and the Zapier automation platform. Here’s Alita to explain what she built. National No Code Day Apps are everywhere! There are apps for banking, organizing, or even just sending reminders to water your plants. They’re so common that small businesses like mine are even making their own apps now to send recurring emails or SMS messages to their clients — after all, it’s an effortless and effective way to stay in touch with customers and to continue to generate new business. It takes very little skill. Take it from me; I’m not tech-savvy at all! As an English teacher, I spend a lot of time sending out homework emails, which hardly anyone actually opens… 😒 Oh, the beauty of teaching. Anyways, I thought that maybe I could automate this process and send out a recurring email to my students with a new English expression for them to practice each week, all without wasting time sending these emails myself. Then, we could have a simple, searchable page with all of the English expressions that go out in these emails, for the students to look through as a reference. The problem is, I don’t have one ounce of developing skill within me other than knowing how to turn computers on and off; use the programs I need for teaching like Google Sheets, Docs, and Slides; and I can send out emails and download homework files. I still don’t really even know what code is. But I went off on this journey anyways, and as it turns out, it’s way easier than it seems! And In its simplest form, it goes like this: if I can do it, you can too! Configure a no-code platform like Zapier Find a tool to walk through this whole process without needing to write any code. I found to be super helpful here. If you’d like to follow along, you’ll need to sign up for an account and create a new Zap. Zapier Select a “trigger”, which is basically the app that will initiate the whole process. I chose Schedule by Zapier, since I want my “Expression of the week” email sent out on a weekly basis. Now we need to find a tool to store our information, one that works with Zapier. For this, I chose Algolia. A friend over there recently showed me that they’re building an Algolia/Zapier connector, and he gave me access to an early version of it. If you want to mess around with it too, . you can get access here After that, you’ll need to choose an “action” in Zapier, which is the app that stores your message and sends it out when it gets triggered. If you clicked the link above and walked through the process, you should be able to choose Algolia here. Add your Algolia data source Next, we’ll need to build our data. Algolia will let us fill our database with a , a format that I hadn’t heard of before. JSON file Here’s how it works: First, find a place to write your file. A great option is Microsoft Notepad. Don’t use anything with formatting options like Word or Google Docs. The whole file will be encased in square brackets. is the beginning of the file and is the end. [ ] Inside the square brackets, we’re going to list several , each of which represents one of the English expressions that the students will receive. Objects start and end with curly braces: . You’ll need a comma in between every object. objects {} Be sure to make the JSON organized and well-spaced so you can read it and add to it easily later. Each line inside an object will be a label (in quotes), a colon, and then the value of that label for that object in quotes. My developer friend says it’s custom to make the label start with a lowercase letter, and replace all spaces by capitalizing the first letter of the next words. So a label could be , but not . You’ll also need a comma in between every line inside an object, so all but the last line inside an object will end in a comma. JSON to describe a person might look something like this: expressionOfTheWeek Expression Of The Week {
    "firstName": "Jane",
    "lastName": "Doe"
} In my case, I want Algolia and Zapier to know which information to send to my students on which day, so I have a label on all of the objects in the JSON file, and their values are the dates I want those expressions sent out on. For testing purposes, it’s best to have one object sent out on the day that you’re making your App. Let me show you what my JSON looked like: day Save this as a file. The app you’re using to write it might make you save it as first, but you can rename it later in the screen with all your computer files too. Make sure the Type of the file says “JSON file”. .json .txt Go back to Algolia. Once you , it’ll walk you through the process of creating an application and an index. Once it asks you to upload data, just give it the JSON file we made. sign up for an account In the Algolia index, I had to add as an attribute for faceting, which basically means I’ll be able to send Algolia an instruction to filter all of the search results to just the ones for the day I choose. This is under Configuration > Facets > . day attributesForFaceting Back in Zapier, under the Choose Account dropdown of the Algolia connector app, we can give it our Algolia credentials. Now that our JSON file is done and in the right place, have Zapier run a test search in Algolia. If you have a result available for today, Zapier should pull it in and recognize the labels we set up in our JSON file earlier. Next, you just need to set up your email information. Under “Body”, you can write the email message and insert information from Algolia by clicking on “Run Search in Algolia” in the dropdown that shows up when you start typing. Set the Algolia tags the way they would appear in a sentence like in the screenshot below. What we write in the Body box will be exactly as it shows up in the email, so make sure you get your punctuation and grammar on point 🙂 Putting it all together And we’re done! Now test it out by sending it to yourself to see how it looks in our inbox! Mine looked like this: Zapier will send these SMS or emails out however often you’d like (with a Zapier subscription since we’re using more than the free tier features). Algolia’s main use is that once you have your index set up, they provide all the necessary tools to let your users (or your app, however it works) search through that data. There’s an option under the UI Demos tab in the index to actually make a prototype of a page where you could search through the information in the index, and that page is good enough for me to send to my students so they can automatically see all of the expressions and search through them without having to go through their emails. It’s : a clean, simple display Isn’t it crazy that I was able to do this with such little experience? It just goes to show that the few hours I put into this (even though that’s probably a little longer than most professional developers would take) were well worth it. After all, technology is constantly advancing, and time is becoming more and more valuable, so learning how to get started with automation is extremely valuable. Thanks to the folks at Algolia for giving me some pointers so that going forward, I could focus on giving my clients high-quality services instead of manually sending out repetitive emails. I’m looking forward to seeing my students make that extra push to use everyday English expressions at work with confidence, all thanks to automation like this. So if you’re a non-developer like me, don’t be scared of tools like Algolia just because they’re built to be coder-friendly. You can still automate your business and spend your time on the more important things with just a small time investment by using low-code organizational tools like Zapier. That power is all at our (non-coding) fingertips! So why not give it a try! If you need any help, the folks at Algolia will be just as happy to as they were with me 😉 point you in the right direction Also published . here

Learning English with Zapier and Algolia

You may have noticed this already, but HackerNoon has a new and improved search experience, thanks to . Lets break down this this much improved search and story curation experience! Algolia Now, as soon as you start typing, you will immediately see results show in a large window.  Character by character they evolve. The content that can be searched has expanded to cover not only stories, but also our tags, companies, and the . ✨ beautiful people of HackerNoon Previously we noticed that a sizable number of search queries were coming back with zero results - primarily for company names.  Now you’ll see them listed in the instant search window, as well as on .  You will find these new sections if you scroll down past the stories. https://hackernoon.com/search Both of these designs are a work in progress.  We’ll be adding tabs to at least the /search page, to better communicate that we support searching these categories now and to help you filter out down to the most relevant result.  I am also interested in including more text in each record so that searches can match content by say, a story’s text content itself.  Algolia offers the ability to show content with matching phrases highlighted and I think this would pair great with searching story content. Related Stories Besides search, Algolia also offers (currently two) AI models that can be trained in such a way as to recommend related stories. The two models are intended for e-commerce sites, which names ‘Related Products’ and ‘Frequently Bought Together’, but the ‘conversion event’ in our case happens when a user completes a story.  When a logged in user reaches the bottom of a story we log this event to further train the model.  This will allow us to better curate which stories the community thinks deserves more readership, as well as, provide based on your reading history. personalized reading suggestions If you don’t want your reading history tracked to have personalized recommendations, you can opt out at . https://app.hackernoon.com/profile/settings Stayed tuned as we soon be deploying more types of pages which will also be searchable, like cryptocurrencies with price charts and related stories, both on HackerNoon and around the web. I also look forward to tweaking the ranking algorithm to highlight our best content, as demonstrated by user engagement. This will serve as a testing ground for personalized content and community-ranked stories, as opposed to simply showing the latest stories per tag and the editor-highlighted stories on the homepage.

Instant Search and Related Stories: Algolia + HackerNoon

Oh Yeah, I've Heard of Algolia Before

Hey Hackers! Here's the tech stack I use to serve up without paying a dime. If you're starting out and don't want to commit to paying monthly server/database costs, some of these resources might be of use to you! The Hive Index Please note that although the website is dynamic in the sense that I update the data source & redeploy frequently, it doesn't have any functionality where the visitors create data. For an application that has user authentication, CRUD resources, or more complex functionality, you might actually need webservers or a "real" database. - Gatsby is a frontend framework for creating static frontend applications (like blogs, documentation sites, portfolio pages). It's based on React, but optimized for runtime performance because the bulk of the work (querying from your database) happens once when you deploy it. Spinning up a website with gatsby is super simple, and they have several that you can clone and modify to make your own. Framework gatsby.js starter projects - Google Sheets My data source is google sheets, which gives me the ability to expand my data source easily, and add fields by simply adding a column in the sheet. It's super easy to work with and you can use to pull data from your private spreadsheet to make the contents of your application. Just note that to update your website, you need to trigger a redeploy of the gatsby application, simply adding a row into the spreadsheet won't update the data on your site. Data Source this plugin - Netlify is a hosting service for static websites. To deploy your site is as simple as pushing up to your github repository, or triggering a build from the Netlify UI. They have a generous free tier, but after a certain amount of deploy minutes or bandwidth of serving your site, you need to pay. In my case, I'm usually at <1% of bandwidth capacity for the month, so until you have a ton of traffic (champagne problems) you don't need to even put in your credit card. Hosting Netlify - My site has a search component, so I'm serving up dynamic content based on the user's search query. This is a bit of a shift from the gatsby way of doing things, which is optimized for knowing the content of your site at buildtime, not runtime. Thankfully, Algolia has a that indexes your site's contents during buildtime, and you can use the algolia frontend client in your react app to serve up the content on your search page. Search Algolia gatsby plugin This has been a nice tech stack that works for me for this particular style of website. For applications that have many more dynamic features I use a different stack (might write a post about that later). Hope this post is useful for you as you spin up your ventures.

ALGOLIA

algolia.com

501-1K employees

Since 2012

COMPANY RANKING

ALGOLIA

EVERGREEN INDEX #1083

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Algolia WIKI

ALGOLIA

algolia.com

501-1K employees

Since 2012

COMPANY RANKING

ALGOLIA

EVERGREEN INDEX #1083

HACKERNOON STORIES ON ALGOLIA

ALGOLIA NEWS & MENTIONS

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Algolia WIKI