One engine. Your full catalog. A ranking for every shopper.
Not a rules engine or segment configurator. The model reads each current session signal and returns a ranked product list in real time.
Turn session signals into a ranked list in one call
This is a continuous model, not a batch job. Every page load gets a fresh ranking for that session.
Shopper arrives
Signals start building when the first page loads: click path, search queries, dwell time, and cart actions.
Score the catalog
The ML ranking model scores every catalog product against current session signals in real time on each page call.
Ranked list returned
Your storefront receives ordered product IDs and renders them as you choose, with no front-end lock-in.
Ranking runs on every page call, not nightly
Your catalog can change by 1,000 products daily, and the engine updates instantly.
Freshness in the model, not the config
Most recommendation systems train nightly and serve cached results. This engine reranks every request. A product added this morning can rank this morning. When a shopper's intent shifts, the next page load gets a different list.
New products, price changes, and inventory shifts reach ranked results immediately. Your merchandising team no longer races to update pages before shoppers see stale ordering.
Keep control of key exceptions
The engine covers your full catalog. You decide which exceptions need your attention.
Pin featured products, exclude clearance items, or boost new arrivals when needed. The engine ranks the rest automatically, with no configuration for most of your assortment.
Five signals, one view of each session
Each signal adds real-time context to ranking. Together, they describe intent better than any single behavior.
Connect your catalog in under an hour
Ranked results from day one, with no backend rewrite. Start a 14-day free trial and measure revenue per session before committing.