The problem it solves

Generic 'customers also bought' widgets rank by global popularity — useless in B2B, where a dental clinic and a distributor should never see the same suggestions. Meanwhile your order history already contains the answer: thousands of accounts whose purchase patterns predict each other's next line item.

What it does for your business

Maps purchase graphs, not just baskets

A neural model learns which accounts buy alike across your whole catalog — so recommendations come from businesses like theirs, not shoppers in general.

Cross-sells at the moment of intent

Suggestions appear where they convert: quote building, cart, reorder screens, and email — each ranked for that specific account.

Works for B2B and B2C on one platform

Account-level recommendations for your wholesale portal; individual personalization for your D2C storefronts — same engine, same eComchain deployment.

Measures itself

Attach-rate and AOV lift tracked against a holdout group, so you see incremental revenue, not vanity clicks.

How it works

Two-tower neural embeddings or collaborative filtering over your account × SKU purchase matrix, retrained as orders accumulate. Served real-time from your eComchain stack with MongoDB Atlas.

What we need from you

Order history (the same export as other models), catalog master, and storefront event tracking (we provide the snippet).

See Recommendation Engine on data like yours

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

Request a Demo