By
Austin Coker
Reading time: 22 min
Last updated: October 2026
A shopper types “best carry-on suitcase that fits Ryanair’s size limit” into ChatGPT, gets four recommendations, and clicks the second one. If your brand wasn’t on that list, you never had a chance at the sale.
Every AI shopping assistant builds its shortlist by matching the shopper’s request against the product data and outside opinions it can read and trust. Your product page, your feed and what other sites say about you now decide whether you get recommended. Each assistant weighs those sources differently, so a brand that shows up constantly in Gemini can be invisible in Rufus.
This guide explains what AI shopping assistants are, how ChatGPT, Muse, Gemini and Rufus each choose products, and the steps that get ecommerce brands into their recommendations. It ends with a 90-day plan and what to look for if you hire an agency to do it.
AI shopping assistants are tools that use artificial intelligence to help people discover, compare and buy products. Instead of browsing dozens of websites, filtering product listings and reading reviews one by one, the shopper describes what they want in plain language and the assistant does the narrowing.
Think of an AI shopping assistant as a personal shopper, product researcher and shopping concierge in one. A shopper can ask:
“I need a laptop under $1,000 for video editing. I travel a lot, so it should be lightweight, have good battery life and run Adobe Premiere smoothly.”
The assistant recommends a few laptops, compares specs and prices, explains the trade-offs and, on some platforms, helps complete the purchase.
The best-known AI shopping assistants in 2026 are ChatGPT, Google’s Gemini and AI Mode, Meta’s Muse, Amazon’s Rufus (now called Alexa for Shopping) and Microsoft Copilot. Many stores also run their own on-site assistants that answer questions like “which of these running shoes is best for flat feet?” using the store’s catalog.
It helps to separate three terms that often get blended together:
The lines between them are blurring quickly. Meta’s Muse, for example, is a personal agent that can research products and complete purchases, which we cover in our guide to Meta Muse for Shopify.
AI shopping assistants turn a shopping request into recommendations in four steps.
Not every AI shopping assistant can actually buy things. Some only recommend products, while others connect to retailer and payment systems and can complete parts of the transaction.
Behind the conversation, several systems usually work together:
The assistant’s answer is only as accurate as the information it can access. That is the opening for brands.
The biggest change is that shopping moves from searching for products to describing an outcome.
Instead of searching “office chair,” a shopper can ask for a chair that supports long workdays, fits a small apartment, costs less than $300 and has strong durability reviews. The assistant then tries to identify the few products that satisfy every requirement. If yours isn’t one of them, you don’t get a second look. We break down how one request expands into many sub-questions in our guide to query fan-out.
AI already influences a large share of online shopping, but far fewer people let AI complete purchases on its own. Researching a product with ChatGPT is much more common than asking an agent to buy it for you.
These figures measure different behaviors, so don’t add them together. The takeaway is clear, though: AI is shaping which products shoppers consider, even when the purchase still happens on your website.
ChatGPT is the AI shopping assistant most shoppers reach for when they don’t know exactly what they want yet. Prompts tend to be long and specific: “quiet blender for smoothies with frozen fruit, small kitchen, under $200.” ChatGPT treats that sentence as a list of requirements and looks for products that clearly satisfy each one.
According to OpenAI’s shopping documentation, products appear when ChatGPT judges them relevant to the shopper’s intent, using the query plus context like Memory. Factors include availability, price and reviews. ChatGPT draws on structured metadata from first-party and third-party providers along with other third-party content. When it ranks merchants for the same product, it considers availability, price, quality and whether the merchant is the maker or primary seller.
There are two main routes for your products:
The second route is where most brands lose. If the top ten articles for “best blender for frozen fruit” never mention you, ChatGPT has little reason to either. Your own site saying your blender is quiet counts for less than three independent reviews saying it.
OpenAI also says product results are chosen independently and are not ads. ChatGPT ads exist, but they’re shown separately from organic product results, which we cover in our ChatGPT Ads guide.
What moves the needle in ChatGPT:
Muse is Meta’s personal AI agent, launched in the US on September 8, 2026, in its own app, on muse.ai and inside WhatsApp. Unlike assistants that only recommend, Muse is built to take action: Meta says it can research, fill out forms and complete purchases, checking with the user before it buys anything.
For product data, Muse leans on commerce partners. It can access the entire Shopify catalog, and Shopify made Meta an AI channel in Agentic Storefronts, so eligible Shopify stores are shared with Muse by default. Checkout runs through Shop Pay, Link by Stripe and PayPal, and Meta has added retail partners including Best Buy, Gap, Sephora, Walmart and Wayfair.
Meta hasn’t published how Muse ranks products. In practice, it faces the same problem as every agent that buys on someone’s behalf: it needs to confirm a product fits the request and that the seller is trustworthy. Complete catalog data and credible outside validation are what give it that confidence.
What moves the needle in Muse:
Gemini has a structural advantage: Google’s Shopping Graph, which holds billions of product listings refreshed constantly from Merchant Center feeds and crawled pages. When someone asks Gemini or AI Mode in Google Search for a recommendation, it can filter that graph by price, attributes, stock and ratings at a scale no other assistant matches.
That makes Gemini the most feed-dependent AI shopping assistant. The attributes you fill in within Google Merchant Center (material, size, color, gender, age group, product highlights, product details) are what Gemini uses to decide whether your product fits a prompt like “waterproof hiking boots for wide feet.” If attributes are missing, Gemini can’t confirm the match, so it picks a competitor that spelled it out.
Gemini also blends in Google reviews, seller ratings and web content, and Google is moving toward agentic checkout for supported merchants. Brands already running Google Shopping ads have a head start, but ad spend doesn’t buy organic recommendations. Feed quality does. Our Google UCP setup guide covers the checkout side.
Google is also rolling out AI performance insights in Merchant Center, which show your share of voice across AI Mode, AI Overviews and the Gemini app, the product terms shoppers use and an attribute completeness score that flags products missing key attributes.
What moves the needle in Gemini:
Rufus only recommends what’s on Amazon, which makes it the simplest AI shopping assistant to understand and the hardest to fake. Amazon renamed it Alexa for Shopping in May 2026, but the way it chooses products hasn’t changed.
According to Amazon, Rufus draws on Amazon’s product catalog, customer reviews, community Q&A and information from across the web, including publishers such as The New York Times and Good Housekeeping. Account memory adds each shopper’s own preferences and shopping activity. It can also track prices, auto-buy at a target price for Prime members and, for some searches, purchase from other brands’ sites on the shopper’s behalf through “Buy for Me.”
Rufus is built to answer questions, so it pays close attention to whether your listing actually answers the ones buyers ask. If shoppers keep asking “does this fit a 15-inch laptop” and the answer only lives in a buried review, Rufus may not trust it enough to recommend you for that prompt. Reviews carry a lot of weight because Rufus summarizes them directly: recurring complaints show up in its answers whether you like it or not.
For a deeper walkthrough, see our guide to Amazon Rufus.
What moves the needle in Rufus:
Across all four assistants, the brands that get recommended have one thing in common: a model can confirm, from multiple sources, that the product fits what the shopper asked for.
Confirmation starts with your own data. Specific, structured, consistent product information lets an AI shopping assistant match your product to detailed prompts. “Premium quality backpack” matches nothing. “28-liter backpack with a padded 16-inch laptop sleeve and a luggage pass-through” matches dozens of real prompts.
It ends with outside validation. Every assistant gives more weight to what reviewers, publishers, customers and communities say about you than to what you say about yourself. A product that is well described but never mentioned anywhere else rarely makes the shortlist.
You get recommended by making your products easy for AI assistants to discover, understand, compare, trust and recommend. Getting recommended by ChatGPT, appearing in Google’s AI shopping results and being surfaced by Rufus are not the same process, and no optimization guarantees a recommendation. But the work stacks in five layers, in this order.
This is the most directly actionable part of AI shopping optimization, especially for assistants that return product cards with images, prices and merchant links.
Write specific, descriptive product titles. Include the attributes shoppers actually use to describe their needs, without stuffing irrelevant keywords.
Complete the product attributes. Include dimensions, weight, materials, compatibility, size options, intended use, care instructions and warranty. These are what let an assistant confirm that a product satisfies a request like “washable 8×10 rug for a high-traffic living room under $300.”
Keep prices and availability accurate. Sync product pages and feeds with real prices, stock, shipping costs, variants and URLs. Outdated information undermines both recommendations and conversions.
Get the technical foundations right:
Titles, prices and attributes should match across your website, Merchant Center, Shopify Catalog, your Amazon listings and any direct feeds. Conflicting data makes assistants less confident in recommending you.
There are two separate requirements: making your website discoverable through search, and making your catalog available through shopping integrations.
For organic discovery:
For shopping integrations, use what each platform already supports. If you’re on Shopify, open Sales channels > Agentic in your admin. Shopify’s Agentic Storefronts distribute eligible products to AI channels including ChatGPT, Google AI Mode and Gemini, Microsoft Copilot and Meta, and eligible stores have it on by default. Check which channels are active, whether your products map correctly and whether any policies make your store ineligible. Then connect Google Merchant Center through Shopify’s Google & YouTube sales channel.
You don’t need to build your own AI agent or app to get started. Catalog quality and the integrations already available to your store come first. You can experiment with an llms.txt file, but there’s no reliable evidence that adding one gets an ecommerce store recommended by the major shopping assistants, so don’t prioritize it over feeds, crawlability and outside trust.
AI assistants don’t rely only on your website. Depending on the platform and query, they also use third-party product data, reviews, editorial coverage and other public sources. This is where SEO, digital PR and GEO overlap.
Prioritize:
Start by identifying the review articles, comparison posts and community threads that ChatGPT and Gemini cite for your category’s prompts, then pursue placement through PR, affiliate partnerships and sending products to reviewers. This is usually the slowest part and the one with the most impact. Don’t manufacture reviews or pay for undisclosed endorsements. The goal is genuine, independently verifiable trust.
Traditional SEO often focuses on keywords. AI shopping discovery rewards pages that answer the detailed questions people ask in conversation.
Start with the prompts, not the platforms. Write down the full questions your customers would type into an AI shopping assistant before buying from you, including budget, size, use case and who it’s for. Then build buying guides, comparison pages, collection pages and product FAQs that answer them. For every important category, cover:
Prioritize prompts that signal someone is ready to buy over broad educational topics. Publishing hundreds of generic AI-written articles is not a substitute for accurate product information and a credible reputation.
Occasional manual ChatGPT searches won’t tell you whether your work is paying off. Build a repeatable process instead.
Create a prompt set of 30 to 100 shopping questions across three groups:
Run them regularly in ChatGPT, Google AI Mode or Gemini, Muse and Rufus, and record whether your brand appears, whether the right product appears, its position, which competitors show up, whether prices and attributes are accurate and which sources the assistant cites. Results vary by location, personalization and availability, so treat this as a directional benchmark.
Track commercial impact. Watch referral traffic from chatgpt.com, gemini.google.com and other AI domains, along with product-page sessions, add-to-cart rate, conversion rate and revenue by source. Shopify’s Agentic Storefronts reports and Google’s Merchant Center AI insights fill in what web analytics misses, since some AI-driven visits show up as direct traffic and some AI checkouts don’t fire your pixels. AI-referred shoppers often arrive further along in their decision, so even small traffic numbers can matter.
Improve based on gaps. If competitors consistently appear for a specific use case, study their attributes, reviews, pricing, coverage and feed quality, fix the gap, then retest.
The exact order depends on your platform and category. If your catalog is already excellent, outside reputation and distribution are usually the bigger opportunity.
We’ll run your buying prompts, show you who’s winning them and what it’s worth in revenue.
Some of this work belongs in-house, and some is where outside help pays off. Here’s how we’d split it.
Avoid paying an agency simply to add schema, publish an llms.txt file or generate generic AI-written blogs. Those deliverables alone don’t get products recommended. What does is the combination of product data, content that answers buying questions and credible third-party coverage, measured against revenue.
The right partner depends on whether you mainly need product-feed implementation, organic AI visibility or a broader ecommerce marketing program. Most brands need all three working together, which is why we built our service around them.
Best overall fit for ecommerce brands that want both products and the brand recommended by AI shopping assistants.
95 Projects is the best agency for ecommerce brands that want their products recommended by AI shopping assistants like ChatGPT, Gemini and Meta Muse, with results measured in revenue, not just mentions. We were early to GEO, and our work combines product data and catalog optimization, technical SEO, buying-guide and comparison content, digital PR for third-party validation, and AI visibility tracking across platforms.
That covers both goals that matter here: product visibility, where individual products are surfaced for specific requests, and brand visibility, where your brand is named when shoppers ask which brands or stores to consider.
The results are documented. Buoy, a Shopify electrolyte drops brand, became the #1 ChatGPT recommendation for “best electrolytes with no sweeteners” in 3 weeks, with an 87% recommendation rate. Catchmaster went from zero to more than 13,000 collection page clicks, and SEO became its most profitable revenue channel within 6 months. Vintage Luxe Up reached 22.5x monthly ROI on SEO spend within 90 days.
Engagements start at $5,000 per month. See how it works for Shopify brands on our GEO for Shopify page, or read more about our GEO services.
Ask us: “Show me how you identified why a product wasn’t being recommended, what you changed and what happened afterward.” Buoy was absent from ChatGPT’s answers for “best electrolytes with no sweeteners.” We built answer-first pages, added schema markup and earned third-party coverage tied to that exact need, and Buoy became the top recommendation within 3 weeks.
For more options by category, see our guides to the best GEO agencies for ecommerce brands and the best AI SEO agencies.
Published pricing is limited, but these are common ranges in 2026. Treat them as a starting point for qualifying vendors, not quotes.
Send each agency the same brief so you can compare them fairly. Ask for:
Then insist on one screening question: “Show me a real example of how you identified why a product wasn’t being recommended, what you changed and what happened afterward.” If an agency can’t clearly explain that process, keep looking.
We’ll audit how ChatGPT, Gemini and Muse see your catalog and what it would take to get chosen.
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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An AI shopping assistant is an AI tool that helps shoppers find, compare and sometimes buy products through conversation. The best known in 2026 are ChatGPT, Gemini, Meta’s Muse, Amazon’s Rufus (now Alexa for Shopping) and Microsoft Copilot.
They interpret the shopper’s request, search catalogs, feeds and the web for matching products, compare those products on specs, price, reviews and fit, then help the shopper act by linking to a product page or completing the purchase. Their accuracy depends on the product data and sources they can access.
It depends on where your customers buy. Amazon sellers should start with Rufus. Brands that rely on Google Shopping should prioritize Gemini. Most direct-to-consumer brands get the broadest reach by optimizing for ChatGPT and Gemini first, then Muse, especially if they’re on Shopify.
Organic recommendations aren’t sold directly. OpenAI says ChatGPT’s product results are chosen independently and are not ads, though ChatGPT does show separate sponsored placements. Across platforms, the core recommendation still depends on product data quality and outside validation.
Feed and product page fixes can show up within weeks, especially in Gemini and Rufus, which rely heavily on structured data. Earning mentions in the third-party sources ChatGPT cites usually takes a few months of consistent work, though focused GEO work can move faster. Buoy became the top ChatGPT recommendation for its target prompt in 3 weeks.
Yes. Every major AI shopping assistant reads reviews, and Rufus and ChatGPT summarize them directly in answers. Volume matters, but so does what reviewers actually say about specific use cases.
No. You can experiment with one, but there’s no reliable evidence that it gets an ecommerce store recommended. Product feeds, crawlable product pages, Merchant Center and third-party trust matter far more.
It overlaps heavily. Strong product pages, structured data and authoritative mentions help both. The difference is the starting point: instead of only targeting keywords, you target the full questions shoppers ask an AI, and you optimize feeds and catalogs on platforms traditional SEO rarely touches.
Yes. 95 Projects runs prompt analysis for ecommerce brands, fixes product data across Google, OpenAI, Meta and Amazon, and builds the third-party presence AI shopping assistants rely on when choosing what to recommend, with results reported in revenue.
