RelevanceRail
RelevanceRail is an AI product recommendation engine for e-commerce. It ranks your catalog for each shopper from their live behavior and purchase history, so recommendations reflect what that person is actually shopping for. The same engine powers product page suggestions, cart cross sells, category ordering, and post purchase offers.
Get a free personalization audit
How does RelevanceRail rank products for each shopper?
RelevanceRail combines three signal layers: what the shopper is doing right now, what they have bought and browsed before, and what similar shoppers chose next. Every page view re ranks the catalog against that profile, so a shopper comparing running shoes never sees dress shoes in their recommendations. Cold start visitors get recommendations from catalog similarity and crowd behavior until their own profile forms.
Where do RelevanceRail recommendations appear?
One engine serves every recommendation surface: product page alternatives and complements, cart page cross sells, collection page ordering, search result ranking, and post purchase follow ups. Because all placements share the same shopper profile, the experience stays coherent: an item dismissed on the product page stops following the shopper into the cart. Merchandisers can pin, boost, or exclude products per placement.
How does RelevanceRail prove its recommendations work?
Every placement runs with a persistent holdout group that sees your original ordering, so lift is measured against your own baseline rather than an industry benchmark. The dashboard reports conversion rate, click through, and revenue per session by placement, and each recommendation can be explained in terms of the signals behind it. When a strategy underperforms, the holdout makes it obvious and the fix is one toggle.
How does RelevanceRail compare to the usual way?
| Capability | Standard storefront | Manual tools and rules | RelevanceRail |
|---|---|---|---|
| Recommendation logic | Bestsellers for all | Manual related product tags | Live ranking per shopper |
| Surfaces covered | One widget | One widget per plugin | Product, cart, category, search, post purchase |
| Coherence | Same item everywhere | Random per placement | Dismissed items stop following shoppers |
| Proof of lift | None | Plugin analytics only | Built in holdout on every placement |
Frequently asked questions
What is an AI product recommendation engine?
An AI product recommendation engine ranks a store's catalog for each individual shopper using behavioral data. It learns from browsing, purchases, and the choices of similar shoppers to predict what a visitor is most likely to buy next, then serves those products across the store. Done well, it replaces generic bestseller widgets with suggestions that feel handpicked for each person.
How is RelevanceRail different from my platform's built in recommendations?
Built in recommendation blocks usually run one simple rule, like items also bought, in one slot on the product page. RelevanceRail runs a full ranking engine across every surface, including cart, collections, search, and post purchase, all sharing one shopper profile. It also includes holdout measurement so you can see exactly what the engine adds over your current setup.
Does RelevanceRail work with my e-commerce platform?
RelevanceRail supports Shopify, Shopify Plus, BigCommerce, WooCommerce, and custom storefronts. Integration is a snippet plus a catalog and order feed, with guided setup for each platform. Search and collection ranking use lightweight APIs that sit alongside your existing theme, so there is no replatforming and no theme rebuild required to go live.
How does the engine handle new products with no sales history?
New products enter recommendations through catalog similarity: attributes, category, price band, and imagery match them to established products with known performance. The engine also gives controlled exploration to new items, showing them to the shoppers most likely to respond, so they earn their own behavioral data quickly. Merchandisers can boost launches manually during the first days.
Can my merchandising team control what gets recommended?
Yes. Merchandisers set guardrails per placement: pinned products, boosted categories, excluded items, and inventory thresholds. The engine ranks freely inside those rules, and every recommendation carries an explanation of the signals behind it. This keeps strategy in human hands while the machine handles per shopper ranking at a scale no team could do manually.
Does RelevanceRail offer a free personalization audit?
Yes. Every engagement with RelevanceRail starts with a free personalization audit of your store. You send your store URL, and the team reviews your traffic patterns, catalog structure, and current customer journeys, then sends back a short report naming the three highest impact personalization moves for your specific store, with concrete examples. The audit costs nothing, needs nothing installed, and the findings are yours to keep whether or not you ever work with RelevanceRail. Request it through the contact link below.
Get a free personalization audit
Send your store URL and get a free personalization audit: the three highest impact moves for your store, in a short report you keep.
Get a free audit