Rufus is Amazon's generative AI shopping assistant. It appears in the Amazon app and on the site as a chat panel where shoppers can ask questions such as "is this jacket warm enough for skiing," "what is the difference between these two blenders" or "what should I look for in a car seat." Rufus answers by drawing on listing content, customer reviews, community Q&A and Amazon's wider product knowledge, and it recommends specific products in its replies.
Why it matters
Rufus changes what a listing is for. A shopper who once scrolled the gallery and skimmed bullets can now ask a direct question and get a direct answer, assembled from your page and from your reviews. If your listing does not state the fact the shopper asks about, Rufus will say so, or will find the answer on a competitor's listing instead. Listings that answer real questions plainly, in the bullets, the A+ Content and the structured attributes, are the ones Rufus can work with.
It also means reviews and Q&A feed conversion in a new way. A recurring complaint in reviews becomes something Rufus may surface when asked, which makes fixing the product or resetting expectations in the copy more urgent than before.
What moves it
- Complete structured attributes. Material, dimensions, compatibility, capacity and care fields are the first place an assistant looks for facts.
- Plain, specific copy. "Fits 13-inch to 15-inch laptops" is usable; "fits most laptops" is not.
- Questions answered on the page. The questions customers ask in Q&A and in reviews tell you what to add.
- Consistency across fields. Contradictions between title, bullets and attributes make the page unreliable.
- Review content, which you cannot write but can influence by fixing what customers complain about.
How we use it
Our AI-readiness work starts by collecting the questions shoppers ask about a product, from Q&A, reviews and search queries, and checking whether the listing answers each one in words. Gaps are filled in the attributes and bullets first, then in A+ Content with alt text. The same edits usually help human conversion, which is how we measure them.