E-Commerce AEO: How to Get Your Products Recommended by AI Search Engines

The AI Shopping Revolution

The way people discover and buy products online is undergoing a fundamental shift. Instead of typing "best wireless headphones" into Google and clicking through ten blue links, a growing number of shoppers are asking ChatGPT, Claude, Perplexity, or Google's AI Overviews — and getting a curated recommendation with specific products, prices, and reasons to buy.

This is not a future prediction. It is happening right now. ChatGPT has over 400 million weekly active users, and product recommendation queries are one of the fastest-growing categories. When someone asks "what is the best coffee maker under $100," AI does not show a list of ads. It analyzes hundreds of data points — product specifications, reviews, pricing, availability, expert opinions — and synthesizes a direct answer: "The Bonavita BV1900TS is widely considered the best drip coffee maker under $100, with a 4.6 rating across 12,000+ reviews..."

For e-commerce brands, this creates both an enormous opportunity and an urgent problem. The opportunity: AI recommendations carry more weight than traditional search results because they feel like trusted advice, not advertising. A product recommended by ChatGPT has implicit credibility that a Google ad never will. The problem: if your products are not optimized for AI, they do not exist in this channel. There is no page two of AI results. Either your product is in the recommendation or it is not.

This is where Answer Engine Optimization (AEO) meets e-commerce. AEO is the practice of optimizing your online presence so AI search engines cite and recommend you. For e-commerce, it means structuring your product data, reviews, content, and technical signals so that when someone asks AI for a product recommendation, your products are in the answer. This guide gives you the complete playbook.

How AI Recommends Products

To get your products cited by AI, you need to understand where AI models get their product data and how they decide which products to recommend. Understanding how AI search engines work is the foundation for everything that follows.

Product Schema and Structured Data

AI models prioritize structured data over unstructured text because it is unambiguous. When your product page has Product schema markup, AI knows the exact product name, brand, price, currency, availability, condition, SKU, and aggregate review score without having to parse HTML. Products with complete schema are significantly more likely to be cited because AI can confidently extract accurate details.

Review Aggregation

AI does not just look at the reviews on your product page. It aggregates review data from multiple sources — your site, Amazon, Best Buy, Wirecutter, Reddit, YouTube reviews, and industry-specific review platforms. A product with 4.5 stars from 2,000 reviews on your site plus 4.4 stars from 8,000 reviews on Amazon plus a Wirecutter recommendation builds a review profile that AI finds highly citeable. Volume, consistency across platforms, and recency all matter.

Comparison and "Best Of" Content

AI search engines heavily reference comparison articles and "best of" lists when forming product recommendations. Content like "Best Running Shoes for Beginners in 2026" or "Coffee Maker Comparison: Drip vs Pour Over vs French Press" gives AI structured, pre-evaluated information it can cite directly. If your brand publishes comparison content featuring your products — or if third-party sites feature your products in their comparisons — AI is more likely to include you.

Brand Authority

AI models assess brand authority through a combination of signals: how often your brand is mentioned across the web, the quality and quantity of backlinks to your site, whether experts and publications reference your products, and how consistently your brand appears in relevant contexts. A brand that is mentioned in Wirecutter, reviewed on YouTube by trusted creators, and discussed in Reddit threads has stronger authority signals than one that only appears on its own website.

Technical Factors

Can AI crawlers actually access your product pages? Many e-commerce sites inadvertently block AI crawlers through their robots.txt configuration. Others use heavy JavaScript rendering that AI crawlers struggle with, or put critical product information behind tabs and accordions that crawlers may not expand. Technical accessibility is a prerequisite — if AI cannot crawl your product pages, no amount of optimization matters.

8 Steps to Get Your Products Cited by AI Search Engines

Step 1: Add Product Schema to Every Product Page

Product schema is the single most impactful technical change you can make for e-commerce AEO. It tells AI exactly what your product is, what it costs, whether it is in stock, and what customers think of it — all in a structured, machine-readable format.

What to include in your Product schema:

• Product name — the exact name customers would search for, not an internal SKU or abbreviated title.
• Brand — critical for branded product queries like "best Nike running shoe" or "is Breville worth it."
• Price and currency — AI models need the exact price to answer queries like "best X under $100."
• Availability — InStock, OutOfStock, PreOrder. AI models deprioritize out-of-stock products.
• Aggregate rating and review count — the most powerful trust signal. A product with 4.7 stars from 3,200 reviews is far more citeable than one with no review data in schema.
• Description — a concise, factual description that includes key specifications and use cases.
• SKU and GTIN — helps AI cross-reference your product across retailers and review platforms.
• Image — a high-quality product image URL.

Many e-commerce platforms add basic Product schema automatically, but it is almost always incomplete. Shopify's default schema often lacks aggregate ratings, brand, and detailed offers. WooCommerce plugins vary wildly in completeness. Always audit your existing schema and fill in the gaps. You can generate complete Product schema with our free schema generator tool.

Step 2: Write Comparison-Friendly Product Descriptions

AI models are constantly answering comparison queries — "X vs Y," "best X for Y," "is X worth it?" Your product descriptions should be structured to make comparison easy.

How to structure product descriptions for AI:

• Lead with key specifications in a structured format. Instead of burying specs in a paragraph, use a clear list: weight, dimensions, materials, capacity, battery life, or whatever attributes matter in your category.
• State who the product is for. "Designed for beginner runners who need extra cushioning" is infinitely more useful to AI than "the ultimate running experience." AI needs to match products to specific user needs.
• Include measurable differentiators. "12-hour battery life" is citable. "Long-lasting battery" is not. "Holds 10 cups" is citable. "Large capacity" is not. AI prefers specific, verifiable claims.
• Address common comparison points. If customers frequently compare your product to a competitor, address the comparison directly: "Unlike [competitor], the [your product] includes [feature]." This gives AI the exact comparison data it needs.
• Add a "Best For" section. A brief section like "Best for: beginner runners, people with wide feet, budget-conscious shoppers" maps directly to how people query AI.

Think of your product description as a brief for an AI that has been asked "Is [your product] a good choice for [specific use case]?" Give it every data point it needs to answer yes.

Step 3: Build Review Volume Across Platforms

Reviews are the social proof engine behind AI product recommendations. When someone asks "best wireless earbuds under $50," AI does not randomly pick a product. It evaluates review volume, ratings, recency, and consistency across platforms to determine which products are genuinely well-regarded.

A single platform is not enough. AI cross-references reviews from your own site, Amazon, Best Buy, Target, Reddit discussions, YouTube reviews, and expert publications. A product with strong reviews on only one platform looks less trustworthy than one with consistent positive reviews across multiple channels.

Your e-commerce review strategy should include:

• On-site reviews — implement a review system on your own product pages. Use schema to mark up aggregate ratings. Make leaving a review as frictionless as possible — post-purchase email, QR code in packaging, one-click rating.
• Amazon reviews — if you sell on Amazon, review volume there directly impacts AI citations. Enroll in Amazon Vine if eligible. Follow up with customers via Amazon's approved messaging.
• Third-party review sites — get your product listed and reviewed on platforms like Wirecutter, Tom's Guide, CNET, or niche review sites in your category. A single expert review from a trusted publication carries enormous weight with AI.
• YouTube and social proof — AI models increasingly reference YouTube reviews and social media sentiment. Encourage creator reviews and unboxing content.

The compounding effect is powerful. A product with 500 reviews on your site, 2,000 on Amazon, a Wirecutter pick, and multiple YouTube reviews creates an overwhelming body of evidence that AI will cite with confidence.

Step 4: Create Category Guides and "Best Of" Content

One of the most effective e-commerce AEO strategies is creating comparison and category guide content on your own site. This serves two purposes: it gives AI a ready-made recommendation structure, and it positions your brand as an authority in your product category.

Types of content to create:

• Category buying guides: "How to Choose the Right Running Shoe" — educational content that naturally positions your products as solutions to specific needs.
• Best-of lists: "Best Coffee Makers for Small Kitchens in 2026" — include your product alongside competitors with honest comparisons. AI values content that compares multiple options, not content that only promotes one.
• Use-case guides: "Best Gifts for Coffee Lovers Under $50" — these map directly to how people query AI. Someone asking ChatGPT for gift recommendations is a high-intent buyer.
• Comparison pages: "[Your Product] vs [Competitor]: Which is Right for You?" — direct comparison content answers the exact queries AI receives most frequently.

Structure every guide with clear headings, product names, prices, ratings, and specific pros and cons. The easier it is for AI to extract structured information from your content, the more likely it is to cite you. This is a core principle of AEO — write content that answers questions directly and structures data for extraction.

Step 5: Add FAQ Schema to Product Pages

Every product page should have FAQ schema answering the questions customers actually ask about that product. FAQ schema provides AI with ready-made question-and-answer pairs it can cite directly — and it is one of the most underused tactics in e-commerce.

Questions to answer in product page FAQ schema:

• Sizing and fit: "Does the [product] run true to size?" "What size should I order if I am between sizes?"
• Compatibility: "Is the [product] compatible with [common accessory/platform]?"
• Use cases: "Is the [product] good for [specific activity]?" "Can I use the [product] for [alternative use]?"
• Comparisons: "How does the [product] compare to the [competitor product]?"
• Care and maintenance: "How do I clean the [product]?" "What is the warranty on the [product]?"
• Shipping and returns: "How long does shipping take?" "What is the return policy?"

You can generate FAQ schema instantly with our free FAQ schema generator. The key is using actual customer questions — pull them from your support tickets, Amazon questions, product reviews, and on-site search queries. These are the exact questions people are asking AI.

Step 6: Optimize for "Best X for Y" Queries

"Best X for Y" is the most common product recommendation query format in AI search. "Best laptop for college students." "Best running shoes for flat feet." "Best coffee maker for small kitchens." Optimizing your product pages and content for this query pattern is one of the highest-ROI AEO activities.

How to optimize for "best X for Y" queries:

• Identify your "for Y" modifiers. What specific audiences, use cases, budgets, or requirements does your product serve? Make a list. For running shoes, this might be: beginners, flat feet, wide feet, trail running, under $100, marathon training, heavy runners.
• Include these modifiers in your product page content. Your product description, spec section, and FAQ should explicitly mention who this product is best for. "Designed for beginner runners weighing 180+ lbs who need maximum cushioning" is exactly the kind of specific claim AI cites.
• Create dedicated pages for each modifier. A page titled "Best Running Shoes for Beginners" that features your product alongside competitors — with clear specs and honest assessments — maps directly to the query AI receives.
• Use the modifier in your schema description. The description field in your Product schema should include the use cases and audience your product targets.

The more specific you are, the better. "Best for everyone" means best for no one in AI's eyes. "Best for beginner trail runners who want ankle support under $120" gives AI a precise match it can recommend with confidence.

Step 7: Implement Offer and Price Structured Data

Price is one of the most common qualifiers in AI product queries. "Best headphones under $50." "Good espresso machine under $200." "Affordable standing desk." If your price is not in structured data, AI may not know where your product falls in price ranges — and it cannot recommend you for budget-specific queries.

Offer schema details to include:

• Price and priceCurrency — the exact price in the correct currency. This must match the visible price on your page.
• Availability — InStock, OutOfStock, PreOrder, BackOrder. AI deprioritizes unavailable products.
• PriceValidUntil — especially important for sale pricing. AI knows the offer is current and time-limited.
• Seller — identifies who is selling the product. Important for marketplace sellers.
• ShippingDetails — free shipping is a common AI query qualifier. If you offer free shipping, include it in your structured data.
• ReturnPolicy — signals buyer confidence. A generous return policy is a positive signal AI may factor into recommendations.

Check that your Offer schema is nested correctly within your Product schema. Many e-commerce platforms add Offer data but disconnect it from the Product entity, which reduces its effectiveness. Our schema markup guide walks through the correct nesting structure.

Step 8: Create an llms.txt That Describes Your Product Catalog

An llms.txt file is a plain text file at your website root that gives AI models a structured overview of your brand and product catalog. For e-commerce sites, this is your chance to tell AI exactly who you are, what you sell, your bestsellers, your differentiators, and where to find detailed product information.

Here is an llms.txt template for an e-commerce store:

# TrailRunner Co

> TrailRunner Co makes performance trail running shoes designed for
> beginners and intermediate runners who want durability, comfort,
> and ankle support at accessible price points.

## Product Categories
- Trail Running Shoes (men's, women's, unisex)
- Running Accessories (socks, insoles, laces)
- Hydration Gear (vests, handhelds, bottles)

## Bestsellers
- [TrailRunner X1](https://trailrunner.co/x1): Best-selling beginner
  trail shoe. 4.7 stars, 3,200+ reviews. $89.99.
- [TrailRunner Pro](https://trailrunner.co/pro): Advanced trail shoe
  with carbon plate. 4.6 stars, 1,800+ reviews. $149.99.
- [TrailRunner Lite](https://trailrunner.co/lite): Lightweight speed
  shoe for racing. 4.5 stars, 900+ reviews. $119.99.

## Key Pages
- [All Products](https://trailrunner.co/products): Full catalog
- [Shoe Finder Quiz](https://trailrunner.co/quiz): Find the right shoe
- [Buying Guide](https://trailrunner.co/guides/trail-running-shoes):
  How to choose trail running shoes
- [Size Guide](https://trailrunner.co/size-guide): Sizing information
- [Reviews](https://trailrunner.co/reviews): Customer reviews

## Shipping & Returns
- Free shipping on orders over $75 (US)
- 60-day free returns, no questions asked
- Ships from Portland, OR

## Brand Info
- Founded 2021, Portland, OR
- B Corp certified
- Used by 50,000+ trail runners
- Featured in Runner's World, Trail Runner Magazine, Wirecutter

This gives AI models a structured catalog overview they can reference when answering product recommendation queries. Most e-commerce competitors do not have an llms.txt file, so creating one gives you an immediate edge. The file takes under an hour to create and sits at your website root (yourdomain.com/llms.txt).

Also verify that your robots.txt allows AI crawlers like GPTBot, ClaudeBot, and PerplexityBot to access your product pages. If AI cannot crawl your site, none of these optimizations matter.

Platform-Specific Tips

Shopify

Shopify adds basic Product schema automatically through its default themes, but it is typically incomplete. Here is what Shopify merchants should check and fix:

• Audit your existing schema. View page source on a product page and search for "application/ld+json" to see what Shopify generates. Check if it includes aggregateRating, brand, offers with availability, and SKU/GTIN.
• Use a schema app to fill gaps. Apps like JSON-LD for SEO or Smart SEO can add the missing fields that Shopify's defaults leave out.
• Add FAQ schema to product pages. Shopify does not add this by default. Use a metafield-based approach or a schema app to add FAQ markup.
• Create a blog with comparison content. Shopify's built-in blog is underused by most merchants. Use it for buying guides, comparison posts, and "best of" content.
• Check your robots.txt. Shopify generates robots.txt automatically and it can be customized through the robots.txt.liquid template. Make sure AI crawlers are not blocked.

WooCommerce

WooCommerce schema depends entirely on which plugins you use, which means quality varies widely:

• Choose a reliable schema plugin. Yoast SEO, Rank Math, or Schema Pro all add Product schema to WooCommerce. Check that the plugin includes offers, aggregate ratings, brand, and availability.
• Add brand to every product. WooCommerce does not have a native brand field. Use a plugin or custom taxonomy to add brand data that flows into your schema.
• Enable review schema. WooCommerce has built-in reviews. Make sure your schema plugin marks up the aggregate rating from these reviews.
• Create category description content. WooCommerce category pages support rich descriptions. Use these for comparison content and buying guides.

Custom Builds

If you built your e-commerce site on a custom stack (Next.js, headless CMS, or custom framework), you have the most control but also the most responsibility:

• Implement Product schema in your page templates. Every product page should output complete JSON-LD Product schema. See the template in the next section.
• Ensure server-side rendering. AI crawlers may not execute JavaScript. If your product data is loaded client-side, AI crawlers see empty pages. Use SSR or SSG to ensure product data is in the initial HTML.
• Create an llms.txt file. With a custom build, you have full control over your llms.txt. Make it comprehensive.

Amazon Sellers

If you sell primarily or exclusively on Amazon, your optimization is different because you do not control the page markup. Focus on what you can control:

• Optimize your product title. Include the brand, product type, key differentiator, and primary audience. AI extracts this directly.
• Structure your bullet points. Lead each bullet with a specific, factual claim. "12-hour battery life with ANC on" not "enjoy long-lasting battery."
• Maximize review volume. On Amazon, review count and rating are the strongest signals AI uses. Enroll in Vine, follow up with customers, and create a product that earns organic reviews.
• Use A+ Content. Amazon's A+ Content (formerly Enhanced Brand Content) adds structured information that AI crawlers can access. Use comparison charts, specification tables, and detailed imagery.
• Build your own website too. Having your own site with Product schema, FAQ schema, and comparison content gives AI a second authoritative data source. Brands that only exist on Amazon have limited AEO surface area.

Product Schema Template: Ready-to-Use JSON-LD

Here is a complete Product schema template you can adapt for your product pages. This includes all the fields AI models look for when evaluating products for recommendations. You can also generate this with our free schema generator tool:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "TrailRunner X1 Trail Running Shoe",
  "image": [
    "https://trailrunner.co/images/x1-main.jpg",
    "https://trailrunner.co/images/x1-side.jpg",
    "https://trailrunner.co/images/x1-sole.jpg"
  ],
  "description": "The TrailRunner X1 is a beginner-friendly trail running shoe with maximum cushioning, reinforced ankle support, and a Vibram outsole. Designed for runners 150-220 lbs on moderate terrain. 4mm drop, 10.2 oz (men's size 10).",
  "brand": {
    "@type": "Brand",
    "name": "TrailRunner Co"
  },
  "sku": "TRC-X1-M-BLK-10",
  "gtin13": "0123456789012",
  "color": "Black/Orange",
  "material": "Recycled mesh upper, Vibram outsole",
  "audience": {
    "@type": "PeopleAudience",
    "suggestedGender": "unisex",
    "suggestedAge": "18+"
  },
  "offers": {
    "@type": "Offer",
    "url": "https://trailrunner.co/x1",
    "priceCurrency": "USD",
    "price": "89.99",
    "priceValidUntil": "2026-12-31",
    "availability": "https://schema.org/InStock",
    "itemCondition": "https://schema.org/NewCondition",
    "seller": {
      "@type": "Organization",
      "name": "TrailRunner Co"
    },
    "shippingDetails": {
      "@type": "OfferShippingDetails",
      "shippingRate": {
        "@type": "MonetaryAmount",
        "value": "0",
        "currency": "USD"
      },
      "shippingDestination": {
        "@type": "DefinedRegion",
        "addressCountry": "US"
      },
      "deliveryTime": {
        "@type": "ShippingDeliveryTime",
        "handlingTime": {
          "@type": "QuantitativeValue",
          "minValue": 1,
          "maxValue": 2,
          "unitCode": "DAY"
        },
        "transitTime": {
          "@type": "QuantitativeValue",
          "minValue": 3,
          "maxValue": 7,
          "unitCode": "DAY"
        }
      }
    },
    "hasMerchantReturnPolicy": {
      "@type": "MerchantReturnPolicy",
      "applicableCountry": "US",
      "returnPolicyCategory":
        "https://schema.org/MerchantReturnFiniteReturnWindow",
      "merchantReturnDays": 60,
      "returnMethod": "https://schema.org/ReturnByMail",
      "returnFees": "https://schema.org/FreeReturn"
    }
  },
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.7",
    "reviewCount": "3247",
    "bestRating": "5",
    "worstRating": "1"
  },
  "review": [
    {
      "@type": "Review",
      "reviewRating": {
        "@type": "Rating",
        "ratingValue": "5",
        "bestRating": "5"
      },
      "author": {
        "@type": "Person",
        "name": "Sarah M."
      },
      "reviewBody": "Perfect beginner trail shoe. Great ankle support and the cushioning is excellent for longer runs on rocky terrain."
    }
  ]
}
</script>

Notice the level of detail. This schema includes the product name, brand, specific description with measurable specs, complete offer details with shipping and return policy, aggregate ratings, and a sample review. Every field gives AI more data to cite your product confidently. Adapt this template for your products, replacing the placeholder values with your actual data.

Common E-Commerce AEO Mistakes

Even e-commerce brands that invest in SEO make specific mistakes that tank their AI visibility. Here are the most common ones and how to fix them.

No Schema or Incomplete Schema

This is by far the most common issue. Either the site has no Product schema at all, or it has the bare minimum — a product name and price with no brand, no aggregate rating, no availability, and no detailed offers. Incomplete schema is almost as bad as no schema. AI needs comprehensive data to recommend with confidence. Audit every product page. Use our free AEO scanner to check your schema completeness.

Thin Product Descriptions

A product page with a two-sentence description gives AI almost nothing to work with. When someone asks "best wireless earbuds for running," AI needs to know that your earbuds are waterproof, have ear hooks for stability, have a specific battery life, and are designed for active use. If your description just says "Great wireless earbuds with amazing sound," AI cannot match your product to the query. Write descriptions that are at least 150-200 words with specific, measurable claims and clear use-case targeting.

Blocking AI Crawlers

Many e-commerce platforms and CDNs block AI crawlers by default. Check your robots.txt file immediately. If GPTBot, ClaudeBot, or PerplexityBot are disallowed, your products are invisible to those AI search engines regardless of how well you have optimized everything else. This is the easiest fix on this list and the most consequential if you are blocking crawlers without realizing it.

No Review Strategy

Products with zero or very few reviews are almost never recommended by AI. AI needs social proof to make confident recommendations. If your competitors have 2,000 reviews and you have 15, AI will recommend them every time. Implement a systematic review collection process: post-purchase emails, in-package inserts, and follow-up reminders. Make leaving a review take under 30 seconds.

No Comparison Content

If the only content on your site is product pages, you are missing the entire "best X for Y" query category. AI heavily references comparison content when making recommendations. Create buying guides, comparison pages, and "best of" lists. Yes, including competitor products. AI trusts balanced comparison content more than content that only promotes one brand.

Client-Side Rendering Only

If your product pages load content via JavaScript and the initial HTML is empty or minimal, AI crawlers may see blank pages. AI crawlers do not always execute JavaScript. Ensure your product data, schema, and descriptions are present in the server-rendered HTML. Test by viewing your page source — if you do not see your product data in the HTML, neither does AI.

Frequently Asked Questions

How do AI search engines recommend products?

AI search engines recommend products by aggregating data from multiple sources: product pages with structured schema markup, review platforms (your site, Amazon, expert publications), comparison articles and "best of" lists, and brand authority signals. They synthesize this data to identify products that best match a specific query — "best running shoe for beginners under $100" — and recommend two to five products with specific reasons why each fits the criteria.

What is Product schema and why does it matter for AI search?

Product schema is structured data markup (JSON-LD code) that tells AI models exactly what your product is, its price, availability, brand, reviews, and specifications. Without it, AI has to guess this information from unstructured page content — and it frequently gets details wrong or skips the product entirely. With Product schema, you hand AI the exact data it needs in the exact format it prefers. You can create yours with our free schema generator.

Do Shopify stores need AEO optimization?

Yes. While Shopify adds basic Product schema automatically, it is often incomplete — missing aggregate ratings, detailed brand information, and offer details like shipping and return policies that AI models rely on for confident recommendations. Shopify stores also lack FAQ schema on product pages, comparison content, and an llms.txt file by default. These gaps mean Shopify stores are leaving significant AI visibility on the table without additional optimization.

What is llms.txt and why do e-commerce sites need one?

An llms.txt file is a plain text file at your website root (yourdomain.com/llms.txt) that gives AI models a structured summary of your brand, product catalog, bestsellers, and key pages. For e-commerce sites, it serves as a catalog overview that AI can reference when answering product recommendation queries. Most competitors do not have one, so creating an llms.txt file provides an immediate advantage in AI product recommendations.

How do I get my products recommended when someone asks ChatGPT for the "best X"?

To get cited in "best X" queries, focus on three areas: complete Product schema with price, availability, aggregate reviews, and detailed specs; review volume across multiple platforms including your site, Amazon, and expert publications; and comparison-friendly content that positions your product with clear differentiators. Also create "best of" category guides on your own site and add FAQ schema answering common product questions. Follow our complete AEO checklist for the full optimization strategy.

Does Amazon product optimization help with AI search?

Yes. AI search engines pull product data directly from Amazon listings — titles, bullet points, descriptions, reviews, ratings, and pricing. Optimizing your Amazon listings with specific, structured information improves your chances of being cited in AI recommendations. However, relying only on Amazon limits your AEO surface area. Maintaining your own website with Product schema, FAQ schema, comparison content, and an llms.txt file gives AI multiple authoritative sources to reference.

The E-Commerce Brands That Start Now Will Win

AI product recommendations are not a future trend. They are happening right now, at massive scale. Hundreds of millions of people are asking ChatGPT, Claude, Perplexity, and Google AI Overviews to recommend products — and those AI models are pulling from product pages, reviews, comparison content, and structured data to form their answers.

The vast majority of e-commerce brands have done zero AEO work. They are relying on default platform schema that is incomplete, thin product descriptions that give AI nothing to work with, and zero comparison content. They have no FAQ schema on product pages, no llms.txt file, and in many cases, they are blocking AI crawlers without even knowing it.

Every step you implement from this guide puts distance between you and your competitors. Start with the highest-impact changes: audit and complete your Product schema (Step 1), write comparison-friendly descriptions (Step 2), and create an llms.txt file describing your catalog (Step 8). These three changes alone can dramatically increase your AI visibility.

Then build out the remaining steps: review volume, category guides, FAQ schema, "best X for Y" optimization, and offer structured data. Each step compounds. Together, they create a product discovery channel that grows as AI search grows — and AI search is growing faster than anything in e-commerce since mobile.

Want to see how AI-visible your product pages are right now? Scan your site free with Vida AEO and get your AI visibility score in under 60 seconds. See exactly which product page optimizations you need and where to start.