A buyer's journey through Shopify isn't a click. It's a sequence—searches, views, add-to-carts, favorites, purchases—spread across storefronts and the Shop app, stretching back months. That sequence carries meaning in its order, timing, and gaps between events.

A good recommendation reads that sequence and predicts what comes next. At Shopify's scale (millions of products and billions of events), getting this right requires a model that can work with the full sequence, not just a summary of it.

This is how we built a foundational generative recommender: a system that learns directly from raw event sequences, operates within real production latency constraints, and delivers measurable impact at scale. We'll cover the architecture decisions, the training strategies that actually moved the needle, and what happened when we shipped it.

The scale of the opportunity

The Black Friday Cyber Monday weekend is the clearest reminder that recommendations at Shopify are built in an environment defined by scale. During BFCM 2025, Shopify observed 2.2 trillion edge requests, and more than 81 million consumers bought from Shopify-powered brands. That customer count is based on unique buyer emails associated with purchases made from Shopify merchants over the BFCM weekend.