Amazon Optimisation
Amazon Rufus and the Cosmo Algorithm
Amazon has quietly moved from being a search box with filters to something much closer to a personal shopper that knows you, understands products, and is increasingly willing to act on your behalf.
3 December 2025
12 min read
Two things power that shift:
Rufus – the conversational, generative AI assistant that sits in the shopping experience.
COSMO – Amazon’s AI-driven search algorithm that learns from everything shoppers do to better match intent with products.
Think of Rufus as the front-of-house and COSMO as the back-of-house brain. One talks to customers; the other continuously reorders the shelves.
This blog looks at how they fit together, and what that means for brands trying to stay visible.
1. What is the Cosmo Algorithm?
VML describe COSMO as Amazon’s AI-driven search algorithm that learns from shopper behaviour to build a network of connected information between what a product is and what a shopper really needs.
Instead of just matching “running shoes size 10” to product titles and bullet points, COSMO looks at:
What people click on and dwell on
What they eventually buy after a series of searches
How different attributes co-occur (use case, material, price band, brand, rating, etc.)
Over time, it builds a graph of relationships like:
“Shoppers who search for ‘trail shoes for muddy winter runs’ tend to buy products with deep lugs, waterproof uppers, and high-visibility elements, even if they never type those words.”
That has a few big consequences:
Intent beats exact keywords. COSMO is trying to understand why someone is searching, not just what they type.
Relevance is multi-dimensional. Past behaviour, price sensitivity, typical brands, and context all shape what surfaces.
Keyword stuffing is obsolete. If your listing is awkwardly padded with search terms but doesn’t help COSMO understand who the product is for and why it’s good, you lose.
2. What is Amazon Rufus?
Rufus is Amazon’s generative, agentic AI assistant embedded in the shopping app and website. It’s trained on:
- Amazon’s full product catalogue
- Customer reviews and community Q&A
- Behavioural data from shopping activity
- Information pulled from across the web
It can:
Answer open-ended questions like “What do I need for a Frozen-themed 7th birthday party?”
Compare categories “Trail shoes vs road running shoes for London winter?”
Recommend specific products and add them to your cart
Check if you’re getting the best price, track deals, and even auto-buy when something drops to a target price.
Rufus is built on a stack of large language models (including Amazon’s own and partners like Anthropic), using retrieval-augmented generation and reinforcement learning to stay grounded in real product data and reviews.
In plain language:
Rufus is the chatty layer that understands natural language and turns it into shopping decisions. COSMO is one of the systems that decides which products Rufus should trust and recommend.
3. Rufus + Cosmo: How They Change Discoverability
When you type or say a query in Amazon now, several things happen:
Rufus interprets intent.
It turns “I’m training for my first marathon in winter, need shoes that won’t slip” into a rich intent profile: beginner, road + possibly light trail, cold/wet weather, grip, cushioning, maybe pronation.
Rufus retrieves evidence.
It pulls in candidate products, reviews, Q&A snippets, buying guides, and possibly external web pages.
COSMO re-ranks for relevance.
Based on massive behavioural data, COSMO helps prioritise which products have historically satisfied people with similar missions and constraints.
Rufus explains and acts.
Instead of showing a raw grid of 64 products, it returns a short list with explanations and options to “add all to cart,” “swap this item,” or “show a cheaper alternative”.
The result is a move from “type keywords → scan grid → manually compare” to “describe your situation → get an answer → tweak if needed.”
That radically changes what “discoverability” means:
You’re not just fighting for blue links on a search results page.
You’re fighting to be named in the answer that Rufus gives.
The algorithm isn’t just scoring you on keyword relevance, but on how well you solve a specific mission.
4. What This Means for Brands and Sellers
4.1 Intent-rich content beats keyword lists
COSMO renders simple keyword stuffing obsolete.
- Your titles, bullets, and descriptions should:
- Explicitly connect the product to real missions and problems
- Use the same language shoppers use in questions (how, why, which, for whom)
- Make trade-offs clear (lighter vs more durable, softer vs more supportive, etc.)
Examples of the kind of phrasing COSMO and Rufus can understand:
“Ideal for beginners training 3–4 times per week for their first half marathon.”
“Best for dry trails and light mud – not designed for deep winter bogs.”
“Formulated for sensitive skin; free from common irritants like fragrance and alcohol.”
This is the opposite of “SEO salad”. It’s structured, helpful explanation.
4.2 Images and A+ content must teach the algorithm
VML point out that PDP images and rich media need to actively contribute to how Amazon understands your product, not just how it looks.
Think of each visual asset as training data:
Infographics that clearly label use cases (“for trail running,” “for plantar fasciitis,” “for oily, blemish-prone skin”).
Context shots that show realistic scenarios (school commute, mountain hike, city break, office desk).
Comparison tables that articulate differences within your own range in natural language.
Those elements help Rufus answer questions like:
“Which of these is best for everyday commuting on a bike?”
“Is this suitable for someone with sensitive, redness-prone skin?”
If your images and A+ are generic and aesthetic only, you’re leaving relevance on the table.
4.3 Reviews and Q&A become core optimisation levers
Rufus leans heavily on customer reviews and Q&A as evidence.
That means:
You want reviews that mention context
“I run 4 times a week on wet pavements in Bristol…”
“Bought this for my 7-year-old who hates scratchy fabrics…”
You want Q&A that capture real questions
“Is this warm enough for standing on the touchline for kids’ football in January?”
“Will this fit under a standard office desk?”
Tactically, that could mean:
Follow-up emails or inserts that prompt people to mention how they use the product.
Proactively answering Q&A in a way that mirrors natural shopper phrasing.
You’re not just persuading future humans; you’re training the model that stands between them and your listing.
4.4 Paid media strategy: play where organic can’t win
VML highlight that brands should focus paid activity on high-intent queries where organic doesn’t yet win, creating incremental opportunities.
In a Rufus + COSMO world, that looks like:
Mining real question data (search terms, “related queries,” and conversation logs where available) to spot missions you don’t yet dominate.
Designing Sponsored Products and Sponsored Brands campaigns that map to missions, not just product keywords.
“cold-weather golf kit”
“first 10k training plan essentials”
“starter skincare routine for sensitive skin”
Aligning ad copy and landing PDPs with the same intent language so COSMO sees high relevance and strong post-click behaviour.
You’re essentially paying to “plug gaps” where Rufus might otherwise recommend competitors, then using performance data to inform content upgrades that move you towards organic wins.
4.5 Structured data and off-Amazon signals still matter
Rufus doesn’t only look at what’s inside Amazon; it also pulls information from across the web.
That makes your broader digital footprint part of the optimisation game:
Clear, consistent product naming across your site, retailer sites, and PR
Structured data (schema.org Product, FAQPage, HowTo, etc.) to help external crawlers understand your products
Content that answers “category education” questions that Rufus might reference when educating shoppers
If your D2C site is vague, inconsistent, or missing, you’re losing a chance to influence how your category is described inside Amazon’s AI stack.
5. Practical actions for the next 90 days
Here’s how to start “speaking Rufus & COSMO” this quarter:
Rewrite your top 10 PDPs for mission clarity.
Add one line in the title or first bullet that clearly states: who, when, and why.
Remove obvious keyword stuffing and replace it with concise, natural phrasing.
Turn images into structured signals.
Add at least one “mission infographic” to each key PDP that spells out use cases and benefits in simple language.
Make sure lifestyle images show realistic, recognisable contexts.
Audit reviews and Q&A for your hero products.
Identify the top 10–15 questions people actually ask and the most common usage scenarios mentioned in reviews.
Reflect those questions and scenarios back into your bullets, A+, and ad copy.
Re-frame your ad targeting around missions, not just SKUs.
Build campaigns around shopper jobs (“train for couch-to-5k,” “set up a home office,” “help my teen sleep better”) and measure which missions you can credibly own.
Align off-Amazon content.
Make sure your own site and major retail partners describe products in ways that match the missions you want to win in Amazon.
Closing thought
Rufus and COSMO are not just “another Amazon update.” Together, they represent a deeper shift:
From searching products to solving missions
From optimising listings to training an AI layer that mediates every discovery journey
Brands that treat Amazon like a static shelf with a search bar will slowly fade from view. Brands that learn to speak the language of intent – in copy, images, reviews, and ads – will find themselves repeatedly surfaced by the very systems millions of shoppers now rely on to decide what to buy.
Rufus – the conversational, generative AI assistant that sits in the shopping experience.
COSMO – Amazon’s AI-driven search algorithm that learns from everything shoppers do to better match intent with products.
Think of Rufus as the front-of-house and COSMO as the back-of-house brain. One talks to customers; the other continuously reorders the shelves.
This blog looks at how they fit together, and what that means for brands trying to stay visible.
1. What is the Cosmo Algorithm?
VML describe COSMO as Amazon’s AI-driven search algorithm that learns from shopper behaviour to build a network of connected information between what a product is and what a shopper really needs.
Instead of just matching “running shoes size 10” to product titles and bullet points, COSMO looks at:
What people click on and dwell on
What they eventually buy after a series of searches
How different attributes co-occur (use case, material, price band, brand, rating, etc.)
Over time, it builds a graph of relationships like:
“Shoppers who search for ‘trail shoes for muddy winter runs’ tend to buy products with deep lugs, waterproof uppers, and high-visibility elements, even if they never type those words.”
That has a few big consequences:
Intent beats exact keywords. COSMO is trying to understand why someone is searching, not just what they type.
Relevance is multi-dimensional. Past behaviour, price sensitivity, typical brands, and context all shape what surfaces.
Keyword stuffing is obsolete. If your listing is awkwardly padded with search terms but doesn’t help COSMO understand who the product is for and why it’s good, you lose.
2. What is Amazon Rufus?
Rufus is Amazon’s generative, agentic AI assistant embedded in the shopping app and website. It’s trained on:
- Amazon’s full product catalogue
- Customer reviews and community Q&A
- Behavioural data from shopping activity
- Information pulled from across the web
It can:
Answer open-ended questions like “What do I need for a Frozen-themed 7th birthday party?”
Compare categories “Trail shoes vs road running shoes for London winter?”
Recommend specific products and add them to your cart
Check if you’re getting the best price, track deals, and even auto-buy when something drops to a target price.
Rufus is built on a stack of large language models (including Amazon’s own and partners like Anthropic), using retrieval-augmented generation and reinforcement learning to stay grounded in real product data and reviews.
In plain language:
Rufus is the chatty layer that understands natural language and turns it into shopping decisions. COSMO is one of the systems that decides which products Rufus should trust and recommend.
3. Rufus + Cosmo: How They Change Discoverability
When you type or say a query in Amazon now, several things happen:
Rufus interprets intent.
It turns “I’m training for my first marathon in winter, need shoes that won’t slip” into a rich intent profile: beginner, road + possibly light trail, cold/wet weather, grip, cushioning, maybe pronation.
Rufus retrieves evidence.
It pulls in candidate products, reviews, Q&A snippets, buying guides, and possibly external web pages.
COSMO re-ranks for relevance.
Based on massive behavioural data, COSMO helps prioritise which products have historically satisfied people with similar missions and constraints.
Rufus explains and acts.
Instead of showing a raw grid of 64 products, it returns a short list with explanations and options to “add all to cart,” “swap this item,” or “show a cheaper alternative”.
The result is a move from “type keywords → scan grid → manually compare” to “describe your situation → get an answer → tweak if needed.”
That radically changes what “discoverability” means:
You’re not just fighting for blue links on a search results page.
You’re fighting to be named in the answer that Rufus gives.
The algorithm isn’t just scoring you on keyword relevance, but on how well you solve a specific mission.
4. What This Means for Brands and Sellers
4.1 Intent-rich content beats keyword lists
COSMO renders simple keyword stuffing obsolete.
- Your titles, bullets, and descriptions should:
- Explicitly connect the product to real missions and problems
- Use the same language shoppers use in questions (how, why, which, for whom)
- Make trade-offs clear (lighter vs more durable, softer vs more supportive, etc.)
Examples of the kind of phrasing COSMO and Rufus can understand:
“Ideal for beginners training 3–4 times per week for their first half marathon.”
“Best for dry trails and light mud – not designed for deep winter bogs.”
“Formulated for sensitive skin; free from common irritants like fragrance and alcohol.”
This is the opposite of “SEO salad”. It’s structured, helpful explanation.
4.2 Images and A+ content must teach the algorithm
VML point out that PDP images and rich media need to actively contribute to how Amazon understands your product, not just how it looks.
Think of each visual asset as training data:
Infographics that clearly label use cases (“for trail running,” “for plantar fasciitis,” “for oily, blemish-prone skin”).
Context shots that show realistic scenarios (school commute, mountain hike, city break, office desk).
Comparison tables that articulate differences within your own range in natural language.
Those elements help Rufus answer questions like:
“Which of these is best for everyday commuting on a bike?”
“Is this suitable for someone with sensitive, redness-prone skin?”
If your images and A+ are generic and aesthetic only, you’re leaving relevance on the table.
4.3 Reviews and Q&A become core optimisation levers
Rufus leans heavily on customer reviews and Q&A as evidence.
That means:
You want reviews that mention context
“I run 4 times a week on wet pavements in Bristol…”
“Bought this for my 7-year-old who hates scratchy fabrics…”
You want Q&A that capture real questions
“Is this warm enough for standing on the touchline for kids’ football in January?”
“Will this fit under a standard office desk?”
Tactically, that could mean:
Follow-up emails or inserts that prompt people to mention how they use the product.
Proactively answering Q&A in a way that mirrors natural shopper phrasing.
You’re not just persuading future humans; you’re training the model that stands between them and your listing.
4.4 Paid media strategy: play where organic can’t win
VML highlight that brands should focus paid activity on high-intent queries where organic doesn’t yet win, creating incremental opportunities.
In a Rufus + COSMO world, that looks like:
Mining real question data (search terms, “related queries,” and conversation logs where available) to spot missions you don’t yet dominate.
Designing Sponsored Products and Sponsored Brands campaigns that map to missions, not just product keywords.
“cold-weather golf kit”
“first 10k training plan essentials”
“starter skincare routine for sensitive skin”
Aligning ad copy and landing PDPs with the same intent language so COSMO sees high relevance and strong post-click behaviour.
You’re essentially paying to “plug gaps” where Rufus might otherwise recommend competitors, then using performance data to inform content upgrades that move you towards organic wins.
4.5 Structured data and off-Amazon signals still matter
Rufus doesn’t only look at what’s inside Amazon; it also pulls information from across the web.
That makes your broader digital footprint part of the optimisation game:
Clear, consistent product naming across your site, retailer sites, and PR
Structured data (schema.org Product, FAQPage, HowTo, etc.) to help external crawlers understand your products
Content that answers “category education” questions that Rufus might reference when educating shoppers
If your D2C site is vague, inconsistent, or missing, you’re losing a chance to influence how your category is described inside Amazon’s AI stack.
5. Practical actions for the next 90 days
Here’s how to start “speaking Rufus & COSMO” this quarter:
Rewrite your top 10 PDPs for mission clarity.
Add one line in the title or first bullet that clearly states: who, when, and why.
Remove obvious keyword stuffing and replace it with concise, natural phrasing.
Turn images into structured signals.
Add at least one “mission infographic” to each key PDP that spells out use cases and benefits in simple language.
Make sure lifestyle images show realistic, recognisable contexts.
Audit reviews and Q&A for your hero products.
Identify the top 10–15 questions people actually ask and the most common usage scenarios mentioned in reviews.
Reflect those questions and scenarios back into your bullets, A+, and ad copy.
Re-frame your ad targeting around missions, not just SKUs.
Build campaigns around shopper jobs (“train for couch-to-5k,” “set up a home office,” “help my teen sleep better”) and measure which missions you can credibly own.
Align off-Amazon content.
Make sure your own site and major retail partners describe products in ways that match the missions you want to win in Amazon.
Closing thought
Rufus and COSMO are not just “another Amazon update.” Together, they represent a deeper shift:
From searching products to solving missions
From optimising listings to training an AI layer that mediates every discovery journey
Brands that treat Amazon like a static shelf with a search bar will slowly fade from view. Brands that learn to speak the language of intent – in copy, images, reviews, and ads – will find themselves repeatedly surfaced by the very systems millions of shoppers now rely on to decide what to buy.