Smart Recommendations learn each shopper's taste, size and intent to surface what they'll love next, on the product page, in the cart and everywhere in between. More discovery, bigger baskets, fewer dead ends.
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74% MATCH
71% MATCH
66% MATCHRe-ranked in real time for this shopper
The average recommendation rail shows the same bestsellers to everyone: the wrong size, the wrong taste, the thing they just sent back. Shoppers have trained themselves to scroll straight past it. Prime real estate, wasted on the people most likely to buy.
Every rail re-ranks itself live from the signals each shopper gives, taste, size, intent, visit after visit, so the next product is the one they were about to go looking for.
Bigger baskets when every suggestion genuinely fits.
Shoppers finally click the rails that understand them.
Taste that compounds: they return to be understood again.
One model reads every signal a shopper gives, re-ranks the rail in real time, completes the look around it, and follows them, personal and consistent, across every surface you sell on.
⌕ ⬨74% MATCH71% MATCH66% MATCHRe-ranked in real time for this shopper
Taste, size, what they dwelt on, what they just returned: every signal re-orders the rail in the moment. The next product a shopper sees is the one they were about to go looking for, and it sharpens with every visit.
Not one more product in isolation, the outfit. Kleep pairs the shoes, the bag and the jacket that genuinely go together, turning a single add-to-cart into a styled, higher-value basket.
Homepage, product page, cart, email: one taste graph drives them all. The shopper meets a store that remembers them everywhere, so the experience feels personal instead of pieced together.
Every view, click and basket teaches Kleep what each shopper loves. That taste graph is yours: the tribes they belong to, the products that pull each other through checkout, and exactly who is about to buy.
Segments that beat age and gender.
Kleep clusters shoppers by what they actually love, not their birthday, so you market to taste instead of guesswork.
Bundle the paths shoppers already walk.
Every journey maps the products that pull each other into the basket, so you can bundle, cross-sell and design the path on purpose.
Spend where it actually converts.
The model scores every shopper's next move, so you time the nudge, the email and the offer to the moment they are ready.
Native apps and cartridges for every major commerce platform, SDKs for your mobile apps. Live in days, not roadmaps.
One-click app from the Shopify App Store, theme blocks included, live the same day.
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A certified cartridge that drops into your existing SFCC storefront architecture.
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An extension built for Adobe Commerce, aligned with your deployment workflow.
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A lightweight WordPress plugin that hooks straight into your product pages.
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A native PrestaShop module, installed and configured in minutes.
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A Swift SDK with native components: the full Kleep experience in your iOS app.
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A Kotlin SDK for Android: same model, same accuracy, fully native UI.
Read the docs →Tested against the incumbents, chosen after the numbers came in. Here is how fashion’s favorite brands run sizing on Kleep.
Lacoste ran an extensive accuracy RFP against every sizing player on the market, including its incumbent Fit Analytics. Kleep came out on top by a clear margin, making it the straightforward choice to switch.
Read full storyba&sh A/B-tested Kleep head-to-head against True Fit. Kleep converted more and returned less, with a gap wide enough to settle the decision before the test had even finished.
Read full storyKleep adapted entirely to the Victoria Beckham world, typography, tone and every detail white-labelled to the brand's own studio, giving customers a sizing experience that feels unmistakably Victoria Beckham.
Read full storyKleep cut Soeur's size-related returns, with more of the pieces customers ordered actually staying in their wardrobes, and the storefront live within a single season.
Read full storyTwo ways, and the shopper chooses. Either a 30-second questionnaire (height, weight, age, body shape) or a body scan from two smartphone photos. Our model turns that into precise measurements, then matches them against the actual measurements of each garment, not a generic size chart. The result: a recommendation per product, not per brand.
Across our deployments, brands typically see size-related returns drop 20–50% and a meaningful double-digit conversion lift, usually 15–25%, on sessions that use a recommendation. Standout deployments have reached up to 3.5× conversion among guided shoppers; exact figures depend on your catalogue, category and traffic mix. You'll see the first signal within weeks: we A/B test against your control group from day one, so every number comes from your store, not our slide deck.
Days, not quarters. We ship native apps for Shopify, Salesforce Commerce Cloud, Magento, WooCommerce and PrestaShop, plus iOS and Android SDKs. Most brands go live within two weeks. The longest step is usually your internal QA, not us. No re-platforming, no PDP rebuild.
Photos are processed to extract measurements, then deleted. We keep numbers, not images. Everything is GDPR-compliant, hosted in the EU, and your customer data is never used to train models for other brands or sold to anyone. Your data stays yours, full stop.
Like your brand. The interface is fully white-labelled: typography, colors, tone of voice, every detail adapts to your design system. Victoria Beckham's sizing flow looks designed by Victoria Beckham's studio. Yours will too.
Run the test. That's how most of our clients arrived. Lacoste chose Kleep after an extensive accuracy RFP against the incumbents; ba&sh A/B-tested us head-to-head against True Fit and the gap made the decision. We're happy to be benchmarked: garment-level accuracy is the whole product.
See the product in action and the results it delivers for brands like yours.
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