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Amazon Rufus is Amazon's generative AI shopping assistant - a conversational interface built into the Amazon Shopping app and website that uses semantic understanding, retrieval-augmented generation (RAG), and Amazon's COSMO knowledge graph to recommend products based on what shoppers mean, not just what they type. Since its February 2024 beta launch, more than 300 million customers have used Rufus, and Amazon's Q4 2025 earnings confirmed it generated nearly $12 billion in incremental annualised sales - exceeding the $10 billion pace CEO Andy Jassy projected after Q3. For Amazon sellers, Rufus represents the most significant change to product discovery since the A9 algorithm: listings now need to satisfy a semantic AI layer that reads content like a human, not a keyword matcher scanning for exact terms.
This guide covers how Rufus works under the hood, what distinguishes it from traditional Amazon search, what data it draws from, and what the implications are for listing strategy in 2026.
Amazon Rufus is a generative AI-powered conversational shopping assistant built on Amazon Bedrock that allows shoppers to ask questions, compare products, and get personalised recommendations in natural language. Amazon announced Rufus in beta on February 1, 2024, initially limited to a small group of U.S. customers, before rolling it out to all U.S. users in July 2024. It subsequently expanded across Europe and other markets. As of early 2026, Rufus is fully available in the U.S., UK, Germany, France, Italy, Spain, Canada, and India.
Rufus is not a simple chatbot. It combines five distinct AI systems working in concert, each of which has direct implications for how sellers should think about listing content:
COSMO - short for Common Sense Knowledge Generation - is a large-scale knowledge graph system developed by Amazon scientists and published as peer-reviewed research at ACM SIGMOD 2024. COSMO is the contextual intelligence layer that gives Rufus its ability to understand real-world relationships between products, queries, customer intentions, and use cases. The COSMO research paper confirms that COSMO's knowledge graph spans 18 major product categories and contains millions of knowledge assertions derived from analysing customer behaviour patterns.
COSMO works by building a vast, interconnected web of common-sense relationships. Amazon Science's technical blog post illustrates this with a concrete example: a customer searching for "shoes for a wedding" is almost certainly looking for formal, hard-soled footwear - not trainers. COSMO understands this contextual connection without the customer needing to specify "formal." Similarly, COSMO infers that a pregnant woman searching for shoes likely needs slip-resistant options, even when neither "slip-resistant" nor "safety" appears in the query - using what the researchers call a used_for_audience relationship in the knowledge graph.
The COSMO–Rufus relationship works as a two-layer system:
The practical implication for sellers is significant: optimising for Rufus and COSMO together requires listings that are contextually rich, not just keyword-rich. Content must communicate who the product is for, what problems it solves, and in what real-world situations it belongs - because that is the information COSMO's knowledge graph indexes.
Rufus differs from Amazon's traditional A9/A10 search algorithm in four fundamental ways: it matches meaning instead of words, it operates conversationally instead of transactionally, it personalises at a deeper level, and it works alongside - not instead of - traditional search.
From matching words to understanding meaning. Amazon's A9 algorithm (and its A10 evolution as referred to by some experts) is a keyword-based system that indexes the words in product listings and matches them against the words in a customer's search query. Keywords, conversion rate, sales velocity, and review quality all factor in, but the foundational logic is lexical: it matches text strings. Rufus operates on semantic understanding. Where A9 asks "does this listing contain the words the customer searched for?", Rufus asks "does this product answer what the customer is trying to accomplish?" A customer searching for "quiet vacuum for a small apartment with pets" through A9 sees listings containing those keywords. Rufus interprets the intent - a compact, low-noise appliance effective on pet hair - and surfaces products that fit that need regardless of exact keyword matches.
Conversational vs. transactional interaction. A9 is transactional: query in, ranked list out. Rufus is conversational: a customer describes a problem or goal, and the interaction narrows toward the best answer through follow-up dialogue. The experience is closer to a knowledgeable store assistant than a catalogue index, which means listings need to provide the kind of information a knowledgeable assistant would draw on - use cases, comparisons, context - not just keyword density.
Personalisation at a new depth. Rufus builds what Amazon describes as "account memory" - an ongoing profile of each customer's preferences, past purchases, household context, and interests drawn from their Amazon shopping activity. Amazon has announced that in the coming months, this memory will extend across its broader digital ecosystem, including Kindle, Prime Video, and Audible. The same product listing is surfaced and framed differently for different shoppers based on this accumulated context. For sellers, this means listing content must address multiple buyer personas and use cases to maximise visibility across Rufus's personalised recommendations.
A9/A10 still matters. Amazon's traditional search algorithm has not been replaced. A9/A10 continues to govern standard product results pages, and conventional ranking signals - sales velocity, conversion rate, click-through rate, keyword relevance - remain important. Sellers now optimise for three distinct systems simultaneously: the A9/A10 keyword algorithm, the COSMO semantic knowledge graph, and the Rufus conversational layer. Each rewards different content qualities, which makes listing strategy more complex but also creates differentiation opportunities for sellers who understand all three.
Rufus draws on five primary data sources - product listings, customer reviews, community Q&As, browsing and purchase history, and web content - each of which sellers can directly or indirectly influence. Amazon confirmed these data sources in its original launch announcement and subsequent technical disclosures.
Product listings. Rufus reads titles, bullet points, product descriptions, and Enhanced A+ Content to understand what a product is, what it does, and who it serves. Unlike A9's keyword indexing, Rufus reads listing content the way a human would - looking for clarity, completeness, and genuine relevance to a shopper's need. Sparse, keyword-stuffed, or vague listings give Rufus less to work with, which directly reduces recommendation confidence.
Customer reviews. Rufus mines customer reviews for natural-language, intent-rich content. When customers describe how they use a product, what problems it solved, or what they wish it did differently, they generate exactly the kind of contextual data Rufus uses to match products to queries. A listing with detailed, authentic reviews gives Rufus far more raw material for recommendations than one with thin or generic feedback.
Community Q&As. The Q&A section is a direct input to Rufus's recommendation engine. When Rufus encounters a shopper question it needs to answer, it explicitly references Q&A content for relevant responses. Thorough, well-answered Q&A sections provide Rufus with ready-made, high-confidence information - making Q&A maintenance a direct lever for recommendation visibility.
Customer browsing and purchase history. Rufus personalises recommendations based on each individual's shopping history, meaning the same listing is presented differently - with different features emphasised - to different shoppers based on their accumulated behavioural profile.
Web content. Amazon has confirmed that Rufus is trained on information from across the web, not just internal Amazon data. This extends Rufus's ability to answer questions that go beyond what a product listing alone can address, which means a seller's broader web presence - brand site, editorial coverage, expert mentions - contributes to how Rufus contextualises their products.
More than 300 million customers used Amazon Rufus during 2025, according to Amazon's Q4 2025 earnings materials, making it one of the fastest-adopted AI features in e-commerce. Amazon's November 2025 announcement reported that monthly average users had grown 149% year-over-year and total interactions had increased 210% over the same period - growth rates that signal accelerating adoption, not plateau.
Amazon CEO Andy Jassy stated during Amazon's Q3 2025 earnings call that Rufus was expected to generate more than $10 billion in annual incremental sales. By Q4 2025, that figure had risen to nearly $12 billion in incremental annualised sales, exceeding initial projections. Internal planning documents reported by Business Insider projected that Rufus would contribute over $700 million in operating profit for the year, with expectations of reaching $1.2 billion in profit contributions by 2027. These figures confirm that Amazon views Rufus as a core revenue driver, not an experimental feature.
The most important statistic for sellers: customers who engage with Rufus during their shopping journey are 60% more likely to complete a purchase compared to those who do not. That conversion lift signals that Rufus is successfully bridging the gap between product discovery and purchase decision - and that sellers whose listings perform well within Rufus conversations gain a measurable sales advantage.
Sellers should optimise for Rufus by writing listings in natural, contextually rich language that answers real shopper questions - a fundamentally different approach from the keyword-density tactics that dominated A9 optimisation.
Keyword stuffing is counterproductive with Rufus. Rufus relies on natural language processing to interpret listings. Content written as a string of disconnected keywords is harder for the AI to parse with confidence. Amazon's patent filings indicate that Rufus specifically looks for "noun phrases" - descriptive, contextual phrases that describe the product in human terms - rather than isolated search terms. Effective Rufus optimisation means writing meaning-first content that incorporates relevant keywords naturally, not keyword-first content that gestures at meaning.
Structure listings around real questions. Rufus is built to answer shopper questions. Bullet points and descriptions structured around the questions customers actually ask - phrased in plain language with direct answers - give Rufus high-confidence information to surface. Vague or incomplete listings reduce Rufus's willingness to recommend: it is conservative by design and does not recommend products it cannot confidently explain to a shopper.
Treat reviews and Q&As as recommendation inputs. Generating genuine reviews and actively maintaining a thorough Q&A section are no longer just trust-building exercises. They are direct contributions to how Rufus understands, contextualises, and surfaces a product. Thin review profiles and unanswered Q&As are missed opportunities for Rufus recommendation visibility.
Invest in visual content as AI data. Because Rufus includes Visual Label Tagging, product photography and infographic content feed directly into how Rufus evaluates a listing. Images that show the product in use, communicate key features visually, and provide contextual lifestyle settings give Rufus richer data than white-background product shots alone.
Amazon is expanding Rufus rapidly, with three developments that will reshape seller strategy in 2026 and beyond.
Cross-ecosystem account memory. In November 2025, Amazon announced that Rufus now incorporates account memory based on individual shopping activity, and that in the coming months this memory will extend across Amazon's broader digital ecosystem - Kindle reading habits, Prime Video viewing, Audible listening - to inform shopping recommendations. If a customer has been reading crime thrillers on Kindle, Rufus will factor that into recommendations for gifts, book lights, and reading accessories. For sellers, this means product listings that connect to lifestyle and interest contexts - not just functional attributes - gain an additional discovery pathway.
Agentic AI capabilities. Rufus now performs actions on behalf of shoppers: automatically adding items to carts, reordering past purchases based on conversational prompts, setting price alerts, and handling multi-step shopping tasks including auto-buying when target prices are met. The "Help Me Decide" feature, launched in October 2025, uses AI-driven comparison to guide shoppers overwhelmed by similar choices. Listings with clear, differentiated value propositions perform better in these head-to-head AI comparisons.
Sponsored ads within Rufus conversations. Amazon began testing sponsored ads within Rufus in September 2024, with its API changelog confirming that sponsored ads could appear in Rufus-related placements. Ad Age reported in November 2025 on further details of sponsored brand prompts within Rufus conversations. The intersection of paid and organic visibility within Rufus is still evolving, but sellers who understand both dimensions will be best positioned as that integration develops.
The strategic direction is clear: Rufus is becoming Amazon's primary interface for product discovery. Early investment in understanding and optimising for Rufus creates a compounding advantage as adoption accelerates.
Amazon Rufus is the most significant shift in Amazon product discovery since the introduction of the A9 search algorithm. It replaces keyword matching with semantic understanding, introduces conversational product discovery alongside transactional search, and draws on five distinct data sources - product listings, customer reviews, Q&As, behavioural history, and web content - to generate recommendations.
With over 300 million users, 149% year-over-year growth in monthly active users, and a 60% higher purchase completion rate among Rufus-engaged shoppers, the system generated nearly $12 billion in incremental sales in 2025. Amazon's COSMO knowledge graph powers Rufus's contextual intelligence, meaning sellers must optimise for contextual relevance - who, what, why, and when - not just keyword coverage.
Sellers who adapt their listing strategy to satisfy Rufus's semantic AI layer alongside Amazon's traditional A9/A10 algorithm gain a measurable, compounding advantage in product visibility and conversion.
Is Amazon Rufus available to all customers? Rufus is available in the U.S., UK, Germany, France, Italy, Spain, Canada, and India as of early 2026. Amazon continues to expand its geographic reach and feature set.
Does Rufus replace Amazon's A9/A10 search algorithm? No. Rufus operates alongside the traditional A9/A10 search engine, which continues to power standard product results pages. Rufus has the most impact on exploratory, research-oriented, and complex shopping decisions; high-intent keyword searches still flow through A9/A10.
Does Amazon Rufus show sponsored ads? Amazon began testing sponsored ads within Rufus in September 2024, and has continued expanding this integration. Ads are contextually matched to shopper conversations rather than keyword bids alone.
What is the relationship between Amazon Rufus and COSMO? COSMO is the underlying knowledge graph that gives Rufus its contextual, common-sense understanding of how products relate to human needs and situations. COSMO is the intelligence layer; Rufus is the conversational interface shoppers interact with directly.
Is Amazon Rufus the same as Alexa? No. Alexa is Amazon's voice assistant designed for smart home control and productivity tasks. Rufus is a shopping-specific AI assistant optimised to help customers make purchase decisions within the Amazon ecosystem. They are separate products.
How long do listing changes take to affect Rufus recommendations? Unlike traditional A9 keyword changes, which can influence rankings within 24 hours, the COSMO knowledge graph updates more slowly. Industry analysis suggests allowing 7–14 days for listing changes to be fully reflected in how Rufus interprets and recommends a product.
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