AI & Automation

Computer Vision & Visual Search

Let customers search your catalog with a photo.

The problem

Customers know what they want when they see it, but they cannot describe it in a search box. Good products stay undiscovered because keywords do not capture how they look.

What's included

  • Catalog audit and image pipeline
  • Vision model selection and embedding generation
  • Vector index with automatic catalog updates
  • Search by image interface for your storefront
  • Similar-product recommendations
  • Relevance testing against your own products

How it fits together

Computer Vision & Visual Search architectureCustomer photoVision modelEmbedding indexRanked matchesStorefront
Computer Vision & Visual Search architecture: Customer photo, then Vision model, then Embedding index, then Ranked matches, then Storefront.

The stack

  • Python
  • Computer vision
  • Vector database
  • Shopify
  • Next.js

Typical outcomes

  • Customers find products they could not describe
  • More of your catalog gets seen
  • A search that improves as your catalog grows

Results depend on your starting point and scope.

Common questions

Your existing catalog photos are usually a good start. We review image quality during scoping and flag anything that would limit results.

Yes. It works with Shopify and custom storefronts, and the index can update automatically when products change.

Yes. The same approach applies to visual inspection, document sorting and matching images against a reference library. Describe your use case in the brief.

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