The problem it solves

Out-of-the-box platform search matches text and stops there. It can't know that buyers searching 'pegs' want the refill pack, or that your best-seller belongs above the collector's item. Every mis-ranked search is a customer doing your merchandiser's job — or calling a rep, or leaving.

What it does for your business

Learns from clicks, carts, and orders

A learning-to-rank model trained on your own search clickstream reorders results by real buying behavior — per query.

Re-ranks on top of your existing search

Your search engine still retrieves candidates; the model re-ranks the top results in milliseconds. Native fit with MongoDB Atlas Search.

Improves continuously

Every search session is new training signal. The ranker gets sharper each month your storefront runs — an asset that compounds.

Proves the lift

Measured with standard ranking metrics on held-out queries and A/B tested live — you see search-to-order conversion move, not just anecdotes.

How it works

LambdaMART (gradient-boosted ranking) or a neural ranker trained on impression → click → cart → order signals with position-bias correction. This is the layer where template-platform search consistently underperforms — a working side-by-side demo is available.

What we need from you

Storefront search-event tracking (query, results, position, clicks, orders — we provide instrumentation) and your catalog. 60–90 days of events is enough to start.

See Smart Search Ranking on data like yours

A 30-minute walkthrough on live sample data — including how the model is trained and validated.

Request a Demo