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Coveo has experienced great growth over the last few years, by bringing in new clients, deploying in new regions, integrating new technologies, etc. But the infrastructure on which our offering sits must follow the same trend. This explosion in data and event volumes demands that companies find scalable solutions to match their ambitions. It’s a bit more complicated than simply throwing buzzwords like incantations, so I’ll help you dive a bit into this world.

Coveo’s Query Suggest model provides highly relevant and personalized suggestions as users type. In this blog post, I will explain how Query Suggest works in the back end, and how it uses mutli-threading to provide results at high speed.

Do you want to have better and faster search results in your Coveo-powered catalog search pages? You can do it by creating an indexing pipeline extension (IPE) that identifies and stores all the variations of your partial SKU values.

You feel like you do a decent job at creating new user intefaces or modifying existing ones, but writing tests to cover your changes takes you more time than to write the actual source code. You have experience writing tests for clear cut units of code like functions or classes, but it seems that testing UI is just too different. Having lost all intuition about what needs to be tested, what needs to be mocked, how to split your test cases, or even how to write a simple assertion, you feel lost and unproductive.

If you can relate to any of the above, this article is for you. This post aims to provide a UI testing “booster pack”, all batteries included, that will get you rolling in no time. Having the right mindset and using the right tools will make you almost look forward to writing tests for UIs. Not only because your tests will grant you confidence and improve the quality of the code you produce, but also quite frankly because it will become an enjoyable thing to do.

Making search engines consistently fast is extremely hard. They have tons of interconnected components, and a minor degradation in one part of the system can easily spiral out of control and become a critical performance issue. Caches in particular are especially crucial to get right, because they can cause a great variety of problems: sub-optimal memory usage, query performance degradations, excessive contention in hot paths, etc. In this post, we’ll cover the current state of caches in the Coveo index, as well as the design and implementation of a new caching library able to solve our issues.

At Coveo, we use Prometheus 2 for collecting all of our monitoring metrics. Prometheus is known for being able to handle millions of time series with only a few resources. So when our pod was hitting its 30Gi memory limit, we decided to dive into it to understand how memory is allocated, and get to the root of the issue.

Trying to find a good introduction for this blog post, I did what I do when I need to write a complex piece of code: a Google search! I searched for DevSecOps. One of the first results was this article on the RedHat website.

Basically, Coveo adopted the DevOps principle, meaning that all developers are now taking part in the design, development, deployment and operations of our cloud infrastructure. This allows us to iterate fast, but this also means that the security teams must move fast as well.

To build secure environments while adopting DevOps, we must shift security left, putting it as much as possible in the hands of our developers. Shifting security left is a hot topic in the software industry right now. This post will give you a few ideas. Spoiler: tooling is the key!

I have been recently trying some new technologies, such as our own Coveo Headless Library, which aims to replace Coveo JavaScript Search Framework with a smaller headless state management library, and Svelte, which aims at writing less and clearer code to achieve the same results as a React or VueJS app.

Those two technologies are so great and fun to work with that I could not wait to use them together. So this blog post will do exactly that: Let’s build a minimal search page using Svelte and the Headless library.

The Coveo JavaScript Search Framework, also known as the JSUI, lets you build visually rich, full-featured search interfaces. Thanks to the Hosted Search Pages and the WYSIWYG Interface Editor Coveo platform users (even the ones with no programming skills) can discover their content easily. All the resources used to set up a Hosted search page are now available through the Search Pages API. How can we benefit from this API and ease our JSUI integrations?

Personalization is one of the biggest marketing trends and is becoming the norm in the industry. Customers are becoming more and more impatient when dealing with online experiences. Not only do they want to be treated as individuals, but they’re also expecting brands to show them content that is relevant to them quickly.

Adding product recommendations to your online experience can help you remain relevant across every interaction while determining your customers’ intent to increase conversion and ensure they have the experience they expect. With the help of AI, product recommendations are experiencing a renaissance. While recommended products are now expected, a lot of them are still using static rules, taxonomy or simple page view tracking mechanism. New advancement in data capture and attribution, product embeddings, and multi-device tracking, allows recommenders to be more precise, personalize and efficient. With these new tools in hand, applying the right strategy becomes paramount.