By
Austin Coker
A shopper types “best running shoes for flat feet under $120” into Amazon. They don’t get a grid of 48 sponsored results. They get an answer with three or four products and a short explanation of why each one fits. If your product isn’t in that answer, you weren’t part of the decision.
Amazon Rufus is Amazon’s generative AI shopping assistant. It sits inside the Amazon app and website, answers questions in natural language, compares products, and recommends specific listings based on what a shopper asks. For ecommerce brands, it means a growing share of Amazon purchases now starts with a conversation instead of a keyword search, and the listings Rufus chooses to mention get a real advantage.
In this article, you’ll find how Amazon Rufus works, what information it pulls from when it recommends products, how to optimize your listings for it, and how it fits into a broader AI search strategy that includes ChatGPT, Google AI Mode, and Perplexity.
Amazon Rufus is a conversational shopping assistant built into Amazon. Shoppers open it from the search bar or the chat icon in the app and ask questions the way they would ask a knowledgeable store employee. “What’s the difference between these two air purifiers?” “Is this blender loud?” “What do I need to start a home espresso setup?”
Amazon launched Rufus in beta in the US in February 2024 and rolled it out to all US customers later that year (source: Amazon’s announcement of Rufus). It has since expanded to additional markets, including the UK, Germany, France, Italy, Spain, and India.
Adoption has moved fast. In its Q3 2025 earnings, Amazon reported that around 250 million customers had used Rufus that year, and that customers who used Rufus were significantly more likely to complete a purchase (source: Amazon quarterly results). Amazon leadership also described Rufus as a driver of billions in incremental annualized sales.
For sellers and brands, those numbers matter for one reason. Rufus is no longer an experiment sitting off to the side of the store. It is becoming one of the main ways shoppers discover and narrow down products on Amazon.
Rufus runs on large language models trained on Amazon’s product catalog, customer reviews, community Q&A, and information from across the web. When a shopper asks a question, Rufus interprets what they actually need and then looks for products whose information best supports an answer.
A big part of this is Amazon’s work on understanding shopping context. Amazon Science has published research on COSMO, a knowledge graph for product recommendations, which maps relationships between products and the real-world situations people buy them for. The practical effect is that Rufus can connect a question like “what should I bring for a winter camping trip with kids” to products that never use those exact words in their titles.
This is the biggest shift from traditional Amazon SEO. The A10 search algorithm rewards keyword relevance, sales velocity, and conversion rate. Rufus still cares about performance signals, but it also needs to understand what your product is for, who it’s for, and how it compares. If that information isn’t clearly stated somewhere it can read, Rufus has nothing to build a recommendation on.
The main sources Rufus draws from include:
Your product title, bullet points, and description
A+ Content and the brand story
Backend attributes and category-specific fields
Customer reviews and ratings
Customer questions and answers on the listing
Product images, including text and context visible in them
Information about your brand and product from outside Amazon
Rufus has also picked up features that make it more of a decision-making tool, like price history, side-by-side comparisons, and remembering a shopper’s past preferences. Each of those pushes more of the evaluation process into the conversation, and away from the shopper clicking through listings on their own.
Traditional Amazon search is a ranking problem. You compete for a position on a results page, and the shopper does the comparison work.
With Amazon Rufus, the comparison happens before the shopper sees anything. Rufus reads the question, decides which products are relevant, and explains its reasoning. A shopper might see three products instead of three pages. Being ranked tenth on a search results page still earns some visibility. Not being mentioned in a Rufus answer earns none.
Questions also get much more specific. Keyword searches tend to be short, like “dog bed large.” Rufus questions sound like “what’s a good orthopedic dog bed for a 90 pound senior lab that sheds a lot.” Long, use-case-driven queries like this reward listings that describe real situations, specific buyer types, and concrete product details instead of generic feature lists.
Reviews and Q&A carry more weight too. When Rufus answers “is this easy to clean?”, it often leans on what customers have said. A product with strong specs but vague or mixed reviews on that point can lose out to a competitor whose buyers consistently mention it.
Optimizing for Amazon Rufus is less about adding keywords and more about making sure every question a shopper could reasonably ask about your product has a clear answer somewhere on your listing.
Before you rewrite anything, figure out which questions lead to your category. Ask Rufus directly. Type in the questions your buyers would ask and see which products come back and why. Do the same with your competitors’ product names. Look at the customer questions section of your top competitors’ listings, and read their reviews for recurring concerns. Those patterns become your optimization roadmap.
A bullet that says “600D Oxford fabric” tells Rufus a material. A bullet that says “600D Oxford fabric that holds up to daily use on rocky trails and wipes clean after muddy hikes” tells Rufus who it’s for and what problem it solves. Keep your primary keywords in the title, but make sure your bullets connect features to the situations people buy for.
Backend attributes and category-specific fields are structured data, and LLM-driven systems rely heavily on structured data to understand and compare products. Missing fields like dimensions, material, compatibility, age range, or care instructions can keep your product out of answers where those details are the deciding factor.
Follow Amazon’s listing quality guidelines to review your product titles, bullet points, attributes, images, and other listing elements.
A+ Content is one of the best places to give Rufus comparison-ready information. Comparison charts across your own product line help Rufus recommend the right variant for the right shopper. Modules that explain who the product is for, and who it isn’t for, give Rufus clear language to work with when a shopper asks for something specific.
Every unanswered question on your listing is a gap in what Rufus knows about your product. Answer existing questions with specific, factual responses. If buyers keep asking the same thing, move that answer into your bullets or description so it’s part of the core listing.
You can’t write your own reviews, but you can influence what they talk about. Packaging inserts, follow-up emails through Amazon’s Request a Review feature, and clear setup instructions all shape what customers notice and mention. If reviews keep flagging the same issue, fix the product or the expectation in your listing, because Rufus will surface that complaint to shoppers who ask about it.
Rufus and Amazon’s visual systems can interpret text and context inside images. Infographics that label key specs, show the product in use, and display size comparisons give the system more to work with. Use clear, legible text on your secondary images.
Rufus draws on information from across the web, not only from your listing. Reviews on independent sites, mentions in roundup articles, Reddit discussions, and your own website content all contribute to how AI systems understand your brand. Brands that show up consistently across the web are easier for any AI assistant to recommend with confidence.
This is where generative engine optimization (GEO) comes into the picture. Beyond optimizing your Amazon listing, you need useful product pages, relevant third-party coverage, and consistent information about your brand across the web.
Amazon Rufus is one AI shopping assistant among several. Shoppers are also asking ChatGPT, Google AI Mode, Gemini, and Perplexity which products to buy, and many of those answers link out to Amazon, your own store, or a competitor’s.
Meta Muse adds another dimension to this shift. Meta’s personal AI agent can browse the web, complete tasks, and make purchases with user approval. As AI agents become more involved in researching and buying products, clear product information and easily accessible details become increasingly important. Brands need to consider not just how shoppers discover products through AI-generated answers, but also how AI agents evaluate products when helping people make purchasing decisions.
The good part for brands is that the work overlaps. A product with clear use-case descriptions, complete specs, strong reviews, and consistent third-party mentions is easier to recommend across AI-driven discovery experiences. The difference is where each system looks. Rufus leans heavily on Amazon’s own data. ChatGPT and Perplexity can draw on the open web, including publisher reviews, forums, and brand websites. Google’s AI experiences can combine search results with product information from Google Shopping and Merchant Center.
A complete AI visibility strategy treats your Amazon listing and your off-Amazon presence as one system. Your listing feeds Rufus. Your website, product feeds, PR coverage, and community presence feed other AI discovery systems and can also help Rufus understand your brand.
At 95 Projects, we help ecommerce brands get their products recommended across AI search, from Amazon Rufus to ChatGPT, Google AI Mode, and Perplexity. If you want to see where your products currently show up in AI answers and where competitors are taking your spot, get in touch with our team.
Austin Coker is the CEO and founder of 95 Projects. He has worked in SEO since 2020, when he built and sold an ecommerce brand that grew mainly through search. He founded 95 Projects in 2022 to help SaaS, ecommerce, and B2B service companies show up wherever their buyers search, from Google to ChatGPT and AI Overviews.
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Amazon Rufus is available to shoppers in the US and has expanded to several other markets, including the UK, Germany, France, Italy, Spain, and India. It’s accessible in the Amazon mobile app and on desktop.
Amazon has introduced sponsored placements within the Rufus experience, so ads can appear alongside Rufus responses. The organic recommendations Rufus makes, however, are based on how well your product information answers the shopper’s question.
A10 ranks products on a search results page based on keyword relevance and performance signals like sales and conversion rate. Amazon Rufus interprets natural language questions and recommends a short list of products with an explanation, relying more on context, use cases, reviews, and structured product data.
Yes. Amazon Rufus pulls from customer reviews and Q&A to answer questions about quality, durability, ease of use, and other details shoppers care about. Review content directly shapes how Rufus describes your product.
Ask Rufus the questions your customers would ask, both broad category questions and specific use-case questions, and track which products come back. Repeat this regularly, since answers can change as listings, reviews, and Amazon’s models update.
No. Clear titles, complete attributes, use-case-driven bullets, and strong reviews help both. You keep your core keywords in place and add the context Rufus needs to understand when your product is the right answer.
Not directly, since Rufus is Amazon’s own assistant. But the same foundations, like detailed product information, strong reviews, and a consistent presence across the web, improve how your products are understood and recommended by ChatGPT, Google AI Mode, and Perplexity. As agentic tools like Meta Muse develop, those foundations can also help AI agents evaluate products more effectively when assisting shoppers.
