Visual Search & Similarity

Let a buyer find a product with a picture instead of guessing at the right search term.

The problem it solves

In visually dense catalogs, buyers often recognize what they want on sight but can't put it into the right search words. Keyword search comes up empty or buried, and the shopper gives up before finding the item that was there all along.

What it does for your business

Image in, matches out

A buyer uploads a photo, from a reference image, a customer request, or a competitor's page, and gets the closest matching SKUs from your own catalog.

"More like this" on every product

Surfaces visually similar items directly from a product page, turning a dead end into another path through the catalog.

Offers substitutes when stock runs out

Out-of-stock pages can point to the nearest visual match instead of losing the visit entirely.

Tuned to your catalog

The underlying model is fine-tuned on your own product imagery, so it learns the distinctions that actually matter for your business.

Where this moves the needle

Higher search-to-product-view rate Fewer zero-result searches Lower exit rate on out-of-stock pages Faster time to find on large catalogs

How it works

A vision embedding model is fine-tuned on your catalog imagery, and every product is indexed into a similarity space so any query image can be matched by visual features rather than text tags. The value scales with catalog size and visual density.

What we need from you

Your product image library at a reasonable resolution and the catalog data it maps to. We'll flag any imagery gaps before training starts.

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Visual Search & Similarity

The problem it solves

What it does for your business

Image in, matches out

"More like this" on every product

Offers substitutes when stock runs out

Tuned to your catalog

Where this moves the needle

How it works

What we need from you

See Visual Search & Similarity on data like yours

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