Klevu

Klevu is an AI-powered site search and product-discovery platform built specifically for online stores. When a shopper types into the search bar of an ecommerce site, Klevu is the technology that interprets the query, works out what the shopper likely means, and returns the most relevant products — with fast autocomplete, natural-language understanding, and results that improve as the store's shoppers interact with them. Beyond the search box, it also powers category-page merchandising and product recommendations, so the same relevance engine shapes what shoppers see as they browse. Founded in 2013 in Helsinki, Finland, Klevu concentrates on the retail use case, tuning its machine learning to the way people shop — synonyms, misspellings, product attributes, and buying intent — rather than to generic document search. Its aim is practical: turn more searches into purchases by helping shoppers find what they want faster.

A note on the company's status: Klevu was brought together in 2025 with Searchspring and Intelligent Reach under a new parent, Athos Commerce, which combines several commerce-discovery products under one roof. So while Klevu remains a recognizable product and brand, it now sits inside a larger group focused on ecommerce search, merchandising, and personalization. This kind of consolidation is common as discovery vendors combine to offer broader suites. For a store evaluating Klevu today, the practical questions are the same as ever — how well its search understands shopper intent, how much control merchandisers get over results, and how cleanly it integrates with the ecommerce platform — but it helps to know the product now lives within Athos Commerce rather than as a fully standalone company.

Klevu's closest comparisons are Algolia and Coveo, and the honest distinction is one of focus. Algolia is a fast, flexible, general-purpose search-and-discovery API used across many kinds of sites and apps, not only stores; developers build search experiences on top of it, which gives great flexibility but puts more of the relevance and merchandising work on the team using it. Klevu, by contrast, is built specifically for ecommerce and comes tuned for retail out of the box — it understands product attributes and shopping intent, and gives merchandisers ready-made controls over category pages and results. So the trade-off is roughly retail-specialized and merchandiser-friendly (Klevu) versus general-purpose and developer-flexible (Algolia). Neither is simply better; it depends on whether you want a shopping-tuned tool or a search platform you shape yourself.

Coveo is a different kind of neighbor. It is an enterprise relevance and AI platform that spans ecommerce but also service, workplace, and website search, aimed at large organizations that want one AI relevance layer across many touchpoints. Coveo's strength is breadth and enterprise depth; Klevu's is being purpose-built and quick to deploy for online retail specifically. The useful way to choose among the three is to ask how retail-specific your need is, how much engineering you want to invest, and how broad a set of search problems you are solving. A store that mainly wants excellent, merchandisable product search leans toward Klevu; a team wanting a flexible search API for varied surfaces leans Algolia; a large enterprise standardizing AI relevance across many systems considers Coveo. All three do AI-driven search, but for different buyers.

Getting value from an ecommerce search platform like Klevu depends on the quality of your product data and the care you put into merchandising. AI relevance is only as good as the catalog it reads, so clean, complete product information — accurate titles, attributes, categories, and images — is the foundation; a self-learning engine can adapt to shopper behavior, but it cannot invent attributes that are missing from your data. Beyond that, the real gains come from using the merchandising controls: promoting the right products on key category pages, tuning results for important queries, handling synonyms and zero-result searches, and watching search analytics to see what shoppers look for and fail to find. Search is one of the highest-intent moments on a store — a shopper who searches is telling you exactly what they want — so improving it tends to move conversion directly.

The failures are treating search as a set-and-forget widget, neglecting product data so relevance suffers, ignoring the analytics that reveal failed searches and missed demand, and expecting AI to compensate for a thin or messy catalog. It also pays to be realistic about vendor claims: uplift figures quoted in marketing are best cases from favorable conditions, not guarantees, so measure the effect on your own store rather than assuming a headline number. Used well — with clean data, active merchandising, and attention to search analytics — a platform like Klevu turns the search bar and category pages into a conversion engine that meets high-intent shoppers with the products they came for. Used passively, it is just a search box that happens to have AI behind it.

Klevu is a coined brand name for the Finnish ecommerce search company founded in 2013, now part of Athos Commerce.

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