Six pieces, one loop.
One model per merchant. Yours alone.
Your own model — a dedicated machine learning model, built for your store and serving only your visitors. Your catalog, orders, and shoppers' behavior are never shared with any other merchant. The model learns your catalog structure, your shoppers' behavior patterns, and the quirks of your storefront.
Each next page is personalized.
From the first second a visitor lands, the engine reads their behavior — every hover, scroll, pause, and click. Each next page's recommendations are based on the visitor's full in-session activity, up to the moment that page opens.
ob.session reads 100+ behavioral signals.
100+ behavioral signals per session — clicks, add-to-carts, where the cursor lingers, which images get enlarged, how fast they scroll, when they backtrack, how long they pause on a hero image, and many more. All anonymous — no name, no email, no account ID. The weak signals are what let ob.session read intent even when a visitor hasn't clicked or added anything yet.
Every visitor gets their own recommendations.
As soon as a visitor opens a page, a request goes to the ob.session engine — while the page is still loading. The engine reads the visitor's full in-session activity up to that moment and returns the recommendations for that visitor. The whole loop happens within 200ms.
Widgets, PLP personalization, or both.
Two ways to render the recommendations on your storefront — both delivered by the same pixel, no separate integration. The first is widgets: drop-in recommendation rows that sit inside your existing pages — product detail pages, cart pages, anywhere you want a rec strip. The second is PLP (Product Listing Page) personalization: the products on your collection grids and homepage grids load directly in the personalized order — same grid, same look, same load speed. The personalization happens before the page renders, not after, so there's no flicker, no shuffle, no second load for the visitor.
Picks come from across your catalog.
On widget rows and homepage placements, recommendations aren't limited to the categories a visitor has browsed. The same behavioral signals that shape the recommendations within a category — what they pause on, scroll past, return to — also tell ob.session when interest is shifting elsewhere. A visitor lingering on a wool coat without converting, then scrolling past every coat below, signals that maybe coats aren't it — and the recommendations start pulling from scarves, gloves, or boots.