Get recommended when shoppers ask AI what to buy.
Shoppers are increasingly using AI answer engines like ChatGPT, Google AI Mode and Gemini to discover, compare and choose products. With agentic commerce, AI agents like Muse are also now shopping on their behalf. Stellar AEO Labs helps retail and e-commerce brands to improve product content, structured data, and technical foundations that LLMs rely on, as well as the third-party signals AI engines use to decide which brands to trust and which products to recommend.
A structured, end-to-end AI search optimization program for e-commerce that starts with how your brand performs in AI answers and AI shopping agents today, identifies what is driving that performance across your site and third-party sources AI cites, and turns the findings into an actionable plan for both.
We build a prompt set around how shoppers discover, compare and choose products in your category, then benchmark where your brand appears across AI answer engines, which competitors are surfaced, and which third-party sources are being cited.
We assess your on-site and off-site footprint across Stellar’s 5 AEO pillars, using a detailed rubric built specifically for e-commerce brands. We then prioritize the highest-impact opportunities by expected visibility impact and effort into a roadmap for content, e-commerce, engineering and marketing teams.
We work with your teams to improve what AI reads on your own site: product facts and buying guidance on PDPs and collection pages, structured data and feeds, crawlability and internal linking. As well as consistent product data across Amazon and retail partners.
We identify the reviews, editorials, forums and social content AI is citing in your category, then build a plan to earn mentions and improve sentiment. Including a Reddit strategy, YouTube content plan and editorial target list.
We re-run the benchmark, track changes in brand and product visibility, citations, sentiment and answer accuracy, and use the results to refine the roadmap as products, competitors and AI models change.
AI engines and shopping agents need enough consistent, credible information to determine what a product is, who it is for, how it differs from alternatives and whether its claims are supported. Retail brands lose visibility when those signals are incomplete, inconsistent or not validated independently.
| AI engines and agents need to understand… | Retail brands often lose visibility when… |
|---|---|
| Product attributes: specifications, ingredients or materials, dimensions, compatibility, variants and other decision criteria. | Core product facts are missing, buried in marketing copy or expressed inconsistently across PDPs, structured data and feeds. |
| Fit & differentiation: who the product is for, what need it addresses and how it compares with alternatives. | PDPs explain what the product is, but do not clearly answer shopper needs or comparisons, and who the product is for and who it is not for. |
| Third-party validation: reviews, editorial buying guides, creator coverage, YouTube videos and Reddit discussions that AI cites as independent proof. | The brand is absent from the sources AI cites for its category, or the reviews and threads that do mention it carry weak or negative sentiment. |
| Retailer & marketplace listings: how the product appears on Amazon, retail partners and other channels where AI agents look up and buy. | Titles, attributes, pricing and reviews differ across retailer listings, so AI cannot reconcile them with the brand’s own site. |
| Current product data: price, availability, variants, launches, restocks and discontinued products. | PDPs, structured data and product feeds contain conflicting information, or updates do not propagate reliably across systems. |
Practical guidance on how AI engines and shopping agents find, compare and recommend products.
AI product discovery is shaping which brands get seen in generative answers. Learn 5 tactics top e-commerce brands use to get cited in ChatGPT, Google AI Overviews, Gemini and more.
In e-commerce GEO, the Product Detail Page (PDP) is the primary unit of optimization because AI systems extract, compare, and evaluate products at the SKU level. This article explains what makes a PDP citation-ready and how to structure product data for AI visibility.
AI engines treat commerce sites differently than informational sites. This article explains how AI systems understand, compare, and recommend e-commerce products, and what brands must expose at the product level to be eligible for citation.
AI engines assemble recommendations from the sources they can retrieve and trust for a given shopping question. They look for clear product facts and use cases on your own site, structured data and feeds that confirm price, availability and variants, and independent validation from reviews, editorial buying guides, Reddit and other community discussion. Products that are described clearly and consistently across all of these sources are easier for AI to understand and safer for it to recommend.
Stellar's five-pillar framework assesses each of these signals and identifies where your products are falling short.
SEO focuses on ranking your pages in a list of search results. AEO focuses on whether AI engines and shopping agents mention, recommend and link to your products when they generate an answer. There is overlap, since crawlable pages, good content and authority help both. AEO puts more weight on explicit product facts, comparisons and use-case answers that AI can extract, on structured data and feeds, and on third-party sources such as reviews, editorials, YouTube and Reddit that AI often cites more than brand sites. AEO work generally supports SEO rather than competing with it.
Yes. ChatGPT indexes structured product feeds that merchants share through OpenAI's merchant program, with Shopify and Etsy stores connected through platform integrations. As of summer 2026, most of ChatGPT's shopping recommendations now come from these feeds rather than from crawled web pages. Google AI Mode draws on Google's Shopping Graph, which is built from Merchant Center feeds and the product information brands and retailers publish across the web, including schema markup on product pages. A feed helps get your products considered, but it does not decide whether they are recommended. AI engines also weigh your product page content, reviews and third-party coverage, and inconsistencies between your feed, structured data and site can count against you.
Stellar reviews your feeds alongside your site and off-site footprint so they all tell the same story.
Yes. Reddit is one of the most frequently cited sources in AI answers to product and "best of" questions, because AI engines treat community discussion as independent evidence of how products perform. If your brand is absent from relevant threads, or discussed with negative or outdated sentiment, that can shape how AI describes and ranks your products.
A Reddit strategy is part of Stellar's Authority Signals work: we identify the subreddits and threads AI is citing in your category, assess how your brand shows up, and build a plan for authentic participation and earning positive mentions.
With agentic commerce, AI agents like Muse compare options and can complete purchases on a shopper's behalf. To do that, they utilize your product feeds and PDPs. They need reliable product data to be confident in making a transaction: accurate pricing, availability, variants, shipping and return terms, and consistent information across your site and the retailers that carry your products. When titles, attributes or prices differ across Amazon, retail partners and your own store, agents may choose a competitor or another seller.
Stellar audits consistency across your channels and helps you make your product data clear and machine-readable wherever agents look.
Partially. Shopify provides useful foundations, including basic product schema, sitemaps, default AI discovery files, and integrations with platforms like OpenAI. But assets like llms.txt are the same for every Shopify store, and if all stores have the same basic schema fields, it's not a differentiator. What Shopify provides does not explain what makes your products different, answer your shoppers' specific questions, fill gaps in your structured data, or address how your brand appears in reviews, editorials, YouTube, Reddit and retailer listings. While Shopify provides the technical foundations for AEO, brands still need to do the work to build their content and trust over those foundations.
It depends on the type of change and the AI engine. On-site improvements such as clearer product content, structured data and crawlability fixes can begin showing up in answers from engines that retrieve live web content within a few days to weeks of being recrawled. Off-site changes, such as new reviews, editorial coverage and Reddit mentions, typically take longer to implement, but once done, they can also start showing up within a few days to weeks in certain types of answers. But for AI engines' internal ranking of your brand to update, these third-party changes have to accumulate and be reflected, which often takes a few months.
Stellar re-runs your benchmark at regular intervals so you can see which changes are moving visibility, citations and sentiment.
To get started with optimizing your website for AI search visibility, submit your information here. Stellar will perform a mini-assessment to give you a AEO-readiness score along the different pillars of our framework. Your free report will be emailed to you within 2–3 business days.
Your request has been received. Your AEO score will be emailed to you within 2–3 business days.