The Brooklinen analysis: how a DTC bedding leader ranks in AI shopping

Brooklinen is the direct-to-consumer bedding leader, the kind of brand most shoppers in bedding can name without thinking. That makes it a useful test, because if a well-known brand struggles to appear when a shopper asks an assistant for a product, the problem is not awareness, it is whether the catalog is readable to the model doing the asking.

We gave ChatGPT the questions a buyer asks before buying and counted how many times Brooklinen appeared as a recommendation rather than a passing mention.

The result: 5 of 11

We score the way the assistant behaves, not the way the brand markets. Eight or more out of the set is strong, four to seven is mixed, and under four means the brand is mostly invisible for buying questions.

A 45% result is mixed. Brooklinen appears for the obvious, high-volume questions and disappears on the specific ones, which is the signature of a catalog that carries category-level signals but not the fine-grained attributes a real shopper question depends on.

What the assistant was really testing

The questions span five kinds of intent, and they get harder as they get more specific. There is the broad category question, the use-case question, the comparison question, the attribute-specific question, and the direct brand-recall question. Brands tend to hold up on the first and last and fall apart in the middle, because the middle is where a query depends on material, size, and weave being present as structured data rather than implied in a description.

Why this happens

An assistant does not read a brand's reputation. It reads the catalog, and it can only recommend a product for 'material' if that attribute exists as a field it can match. When the attribute lives only in marketing copy, the model has nothing to match, so it reaches for whichever competitor wrote the attribute down. That is the whole mechanism, and it is why a smaller brand with a cleaner catalog can out-rank a household name inside an assistant.

What would move the score

The fix is the same regardless of where a brand lands. Map the catalog to the Shopify Standard Product Taxonomy, write material, size, and weave and the rest of the category's attributes to the product metafields, give each product a line of genuine context about what it is for, and make sure the pages render that content server-side so an assistant reads it without running JavaScript. None of it touches the brand or the design; it just moves the truth into the layer the model reads.

Curious how your own catalog reads? The free audit runs the same questions against your store and shows where you drop off.