What is Amazon Rufus?

Amazon Rufus is Amazon’s generative AI shopping assistant, built directly into the Amazon Shopping app and desktop experience. Shoppers can ask natural-language questions like “Which running shoes are best for flat feet?” or “What do I need for making smoothies?”, and Rufus responds with guidance, comparisons, and product suggestions without leaving Amazon. If COSMO is Amazon learning what shoppers mean, Rufus is Amazon turning that understanding into a conversational interface that changes how discovery happens. And if you sell on Amazon, you need to treat Rufus like a new shelf. A shelf that speaks. **Why Rufus matters (even if you are already good at Amazon SEO)** For the last decade, optimising for Amazon meant winning keywords, relevance, and conversion. Rufus shifts the game from “ranking for a term” to “being the best answer to a question”. That is a subtle change with massive implications: - Shoppers ask longer, messier questions. - They expect a direct recommendation, not a grid of results. - Your listing is no longer just competing on search results pages. It is competing inside an AI’s reasoning. Amazon itself frames Rufus as part of a broader move towards “generative and agentic” shopping experiences, including more proactive actions (like adding items to basket) in some experiences. **What Rufus can do today** At a practical level, Rufus helps customers: __Research a category__ “Types of coffee machines” or “What should I look for in noise-cancelling headphones?” __Compare options__ “Compare trail shoes and running shoes” or “lip gloss vs lip oil”. __Recommend products for a use case__ “Best gifts for a teacher” or “What do I need for indoor gardening?”. __Answer questions on a product detail page__ It can summarise what customers say and pull from listing info, reviews, and community Q&A. __Improve shopping convenience__ Amazon has publicly discussed more capable experiences such as personalisation based on a shopper’s activity and agentic features in some contexts. Where Rufus pulls information from This is the part most brands miss. Rufus is not just “reading your bullets”. Amazon’s own technical write-up explains that Rufus is trained primarily on shopping data, including Amazon’s catalogue, customer reviews, and community Q&A, and it uses retrieval-augmented generation (RAG) to select helpful evidence before producing an answer. Amazon’s announcements also describe Rufus as using information from across Amazon and the web to help shoppers decide. So your “Rufus visibility” is a combined outcome of: - Your product content (title, bullets, A+ content, images, attributes) - Your review language and recurring themes - Your Q&A coverage (and whether it matches real customer questions) - Your category context (how clearly you fit a use case) - Your price, availability, and variations (because shoppers often ask value questions) How Rufus changes Amazon discovery Traditional Amazon search is query-led. Rufus is intent-led. A normal search might look like: “protein shaker bottle” A Rufus conversation looks like: “I want a shaker that doesn’t leak in my gym bag, is easy to clean, and can handle thick shakes. What should I buy?” That second one is where brands win or lose, because the best “answer” is rarely the product with the most exact-match keywords. It is the product whose page best supports the intent. This is why we keep talking about agentic commerce at Lmo7. The decision layer is moving from people scanning listings to systems interpreting needs. What brands should optimise for (the Rufus-proof checklist) Here is the playbook we are rolling out with clients now. **1. Write bullets that answer questions, not just list features** Most bullets are still “feature dumps”. Rufus rewards “feature + outcome + scenario”. Instead of: “Stainless steel blade” Use: “Stainless steel blade that stays sharp for daily veg prep, ideal if you cook every night and want consistent cuts.” **2. Build a Q&A moat** Rufus is trained on community Q&A signals as part of its shopping knowledge base. Your goal is to proactively answer: - Compatibility questions - Sizing and fit questions - Durability expectations - Returns and warranty anxiety - Common “will this work for…” use cases - If you do not answer these, your competitors or random customers will. **3. Treat reviews like product strategy, not reputation** Rufus pulls heavily from review content and themes. - You cannot “optimise reviews” directly, but you can: - Fix repeated complaints fast (then your review language shifts over time) - Encourage post-purchase education (so users get the intended outcome) - Make expectations explicit on-page (reducing mismatch reviews) **4. Be brutally clear on “who it is for”** Rufus is a discovery engine for use cases. So spell it out: “Best for narrow feet” “Designed for first-time campers” “Ideal for small kitchens” “Great for toddlers learning to drink independently” That sort of phrasing is not fluff anymore. It is retrieval fuel. **5. Make your images do some of the explaining** Rufus sits inside a shopping journey. Even if the answer is strong, the shopper still checks the listing. Your images should cover: - Scale and dimensions - Use case in real life - What is included vs not included - Before and after (if relevant) Simple “how it works” panels If your visuals do not close the loop, Rufus can get the click, but you still lose the sale. Common Rufus questions you should be able to “win” If you sell any meaningful volume, you should map your catalogue against questions like: “What’s the best [product] for [specific scenario]?” “Is this [product] good for beginners?” “What is the difference between [type A] and [type B]?” “Which one is best value for money?” “Will this work with [device/model/material]?” “What should I look for when buying [category]?” Amazon itself gives examples like broad research, comparisons, and product-level questions. If your listing content cannot support those answers, Rufus will find a product that can. **Is Rufus available in the UK?** Yes. Amazon has announced Rufus availability for UK customers in the Amazon Shopping app and on desktop (with guidance on how to access it). Availability and capabilities can vary by device, account, and rollout phase, but the direction is locked in: conversational shopping is now a native Amazon behaviour. **What this means for Amazon marketing in 2026** A few predictions we are already seeing play out: Listing quality becomes a growth lever again Not just “SEO hygiene”, but narrative clarity and intent coverage. Brands that own use cases will compound If Rufus repeatedly learns your product is the best answer for “running in the rain” or “small flat storage”, you build defensibility. The definition of “organic” gets blurrier Rufus sits between search, browse, and recommendation. Brands will need reporting that reflects that reality. Agentic commerce changes merchandising As assistants become more action-oriented, the “best answer” becomes the new “top shelf”. Amazon has explicitly positioned Rufus within generative and agentic shopping experiences. **Final thoughts** Rufus is not just another Amazon feature. It is a new interface layer over the marketplace, and it nudges the shopper journey from browsing to conversing. If you are serious about Amazon growth, you should be asking one question now: When a shopper asks Rufus the exact question that describes my product’s best use case, do I show up as the best answer? If you want help stress-testing your catalogue against Rufus-style queries, that is exactly the kind of work we do at Lmo7 through our Amazon optimisation and AI search visibility frameworks.