Shoppers ask AI what to buy. It names the brands it can read

Amazon works, and it works for Amazon. When a shopper asks an assistant for the best product in your category, the model draws from what it can read across the web. If the richest description of your product lives on Amazon, the recommendation names Amazon and your brand appears as inventory inside their answer. Your own site is where you show up as yourself, with your name on the citation rather than the marketplace's. Both channels work. Only one of them builds your entity.

Yes, and that is the usual shape of it. Most e-commerce brands we work with have a capable marketing team running paid, email, and content, and none of them has had a reason to learn how AI recommendations get assembled yet. We work through your team, not around them. We do the parts that need the specialized knowledge, product entity structure, comparison content the model can read, and measurement, and we hand back the reasoning so your people can carry it. The goal is that your team can run this without us.

It depends on where the details are and what form they take. Ingredients in an image. Specs in a dropdown that loads after the page. Price in a script the model cannot execute. Variant differences explained nowhere. The details exist, but the model cannot read them in the format they are in. The fix is not adding information. It is making the information you already have legible in text the model can parse and compare against a competitor's.

We build a set of product and category queries the way shoppers actually ask them, the product type plus the use case plus the constraints, and run them across the assistants on a schedule. Then we track whether your brand appears, for which products, and against which competitors. The measure is whether the model names your brand when a shopper asks for what you sell, not whether it sends traffic. Here is how we measure whether shoppers find your brand.

No. Start with the products that matter, the ones you want to be known for, the ones where you have a real differentiator, and the ones where a shopper is most likely to ask an assistant for a recommendation. A brand with five legible hero products beats a brand with five hundred pages the model cannot parse. Depth on the products that matter is worth more than breadth across a catalog.

Small brands often do better here. The model is not ranking by market share. It is matching a product to a question, and a specific brand that clearly fits one use case is easier to recommend than a large brand that claims to fit every use case. If you are the obvious answer to "best X for Y under Z," name recognition is not what decides it. Legibility is.

Structural fixes show up in retrieval within weeks, once the product facts, the differentiators, and the comparison points live in text the model can read. Entity accuracy, the model reliably tying your brand to the right category and recommending the right products, moves over months, because it depends on third-party sources and review content catching up. Watch presence first, whether you appear at all for your core product queries, then accuracy, whether it recommends the right products from your line.