How we build composable ecommerce experiences with OpenAI.

OpenAI's models cover a wide range of commerce work, and the embeddings are as useful as the chat models. Embeddings are what let search understand that a customer typing waterproof jacket for hiking should see a hardshell, even though the words do not match.

We use them for semantic search and recommendations, for content generation across large catalogs, and for the internal tools that never get built because they are not quite worth a sprint. We evaluate against your real queries and your real catalog before anything goes live, because relevance that works on a demo and fails on the long tail is worse than no change at all.

Is OpenAI right for my ecommerce brand?

Who it's for

Brands with a catalog big enough that finding things is genuinely hard, or enough repetitive content work to make automation worthwhile.

Large or technical catalogs where terminology varies
Brands where customers describe products differently than you do
Teams with a backlog of small internal tools nobody has built

What it solves

Search only matches keywords, so shoppers miss products you stock
Recommendations are generic and ignore what the customer is actually doing
Product content cannot keep up with the size of the catalog
Useful internal tools never get built because each one is too small to prioritize

Common questions about integrating OpenAI for your ecommerce brand.

What is semantic search and is it better than keyword search?

Semantic search matches meaning rather than words, so a shopper searching for something to keep the rain off can find a hardshell jacket. It is better on catalogs where customers and merchandisers use different vocabulary. On small catalogs with consistent naming, good keyword search is enough.

Should we use OpenAI or a dedicated search platform like Algolia?

Often both. Algolia gives you speed, filtering and merchandiser control. Embeddings improve relevance on the long tail. Replacing a search platform with a model alone usually costs you the merchandising control you actually rely on.

Can we use this without sending customer data to a third party?

You can design around it. Much of the value comes from working with product and catalog data rather than personal data. Where customer data is involved, what gets sent and what is retained is a decision to make deliberately, not a default to accept.

How do you know if it is actually working?

You measure it against the old behaviour on real queries. Search relevance is testable, which makes it one of the better places to start with AI. If a change cannot be measured against a baseline, we would rather not ship it.

How long does an implementation take?

Four to eight weeks for something like semantic search, including evaluation. Content generation projects are faster to build and slower to sign off, because the review process is usually the bottleneck rather than the technology.