Since the launch of AI search, organic traffic has been slipping.
Google queries are intercepted by AI Overviews. More users turn to ChatGPT for answers. And now, AI-powered search engines are starting to carve out commerce market share with shopping features—offering shopping recommendations directly in the interface.
It’s no longer about just ranking on search engines. It’s about being the source these generative engines pull from.
That’s what generative engine optimization is about.
What is Generative Engine Optimization (GEO)?
Generative Engine Optimization (GEO) is the process of optimizing your content, product data, and online presence to be discovered, recommended, or cited by AI-powered systems like ChatGPT, Google AI Overviews, Perplexity, and Amazon Rufus.
Just like SEO helps you rank in search engines, GEO helps you get surfaced in AI-generated answers, product summaries, and conversational results.
How Ecommerce Brands Can Optimize for Generative Engines
As we go through pointers for generative engine optimization, you’ll notice many of them are relevant for SEO as well.
That’s because SEO has evolved from the old keyword-stuffing days to now focusing on intent, with both on-page and off-page signals contributing to discoverability.
Generative Engine Optimization (GEO) takes it a step further—optimizing your product content so it can be accurately understood, cited, and recommended by AI tools like ChatGPT, Google SGE, and Perplexity.
1. Structure Product Data & Schema for AI
To be surfaced by generative engines like ChatGPT or Perplexity, your product data needs to be structured in a way that AI can understand and use confidently.
Generative engines pull from a mix of structured product data, schema markup, and clean formatting to determine what a product is, what it’s for, and how it compares to alternatives.
Here’s how it works:
Enrich your product data with clear attributes
Generative engines rely on structured fields like:
- Product name
- Dimensions
- Material
- Compatibility
- Certifications
- Care instructions
- Target use cases
- Category or subcategory
If these fields are incomplete, inconsistent, or generic, AI models won’t be able to differentiate your product or confidently recommend it. After all, AI doesn’t respond to branding or emotion—it relies on objective data to determine relevance.
Tip: Enrich your product data with meaningful, clearly labeled attributes. Fill in missing details like materials or use cases, and add any supporting information that helps clarify fit and function.
Hypotenuse AI’s data enrichment tools can help standardize and scale this across large catalogs.
Add schema markup to make content machine-readable
Schema helps generative engines understand what’s on your page without having to read all the content.
Using schema types like Product, Offer, AggregateRating, and Review makes your content machine-readable and more likely to be cited or ranked in AI summaries.
Tip: Add ecommerce structured data to your PDPs. Make sure every product page includes up-to-date schema that reflects availability, price, reviews, and core specs.
Write metadata that reinforces relevance
While not a primary signal, meta titles and descriptions can influence how your product appears in AI-generated links or citations. Good metadata improves clarity and intent matching.
Tip: Write clear, product-specific meta titles and descriptions that include relevant keywords and match the language people use when asking AI engines for recommendations.
2. Align Content to Specific Use Cases
We used to search for products by Googling “3-seater couch” or “wireless earbuds” and sifting through hundreds of options to find the right one.
With AI-powered search, those same queries have evolved into “best 3-seater couch that stays cool in summer” or “wireless earbuds that stay in the ears while running”.
This shift means your PDP content needs to speak the same language. Instead of writing for categories or SEO keywords alone, you need to write for real-life scenarios and buyer intent.
A few ways to do this:
Map content to real-world intent
Generative engines look for signals that a product is relevant to the shopper’s specific scenario. That means your content should reflect who your product is for, what problem it solves, and why it’s the right fit.
If the dress is meant for corporate professionals, mention it. If your robot vacuum can climb steps to clean across levels, highlight that.
Tip: Use phrases like “best for...,” “ideal when...,” or “great choice if...” in your titles, descriptions, and bullets. These give AI clear cues to match your product to intent-rich queries.
Show product fit through benefits, not just features
Your PDP needs to include both:
- Factual specs like dimensions, battery life, and material—these give AI engines structured signals to work with.
- Layman-friendly benefits that explain what those specs actually mean for the shopper—this helps AI match your product to real-world queries.
Tip: Turn technical specs into customer-facing outcomes. Don’t just list materials or numbers—connect them to what they actually mean for the user.
For example:
- Instead of “100% cotton,” say “breathable and ideal for summer.”
- Instead of “100ml,” say “TSA-friendly and safe for carry-on.”
If you're working with large volumes of SKUs, you can use Hypotenuse AI's product description generator to help you generate this in bulk as well.
Use product tags to align with shopper intent
Tags like “pet-friendly,” “made for dry climates,” or “compatible with iOS” don’t just help shoppers navigate—they also signal relevance to generative engines.
When use-case tags are applied consistently across your catalog, AI models are better able to associate your products with specific needs and surface them in the right context.
Tip: Use tags intentionally and uniformly. The clearer the signal across your catalog, the easier it is for generative engines to connect your products to real shopping scenarios. Use an AI product tagging tool to help you ensure consistency and accuracy in your product tags.
3. Build Trust Through Reviews, Proof, and Mentions
Do a quick search and you’ll see AI quoting product reviews and forums like Reddit when recommending products.
It’s a clear sign that reviews and brand visibility drive how generative engines decide what to surface.
Collect and display authentic reviews
Besides helping with generative engines, on-site reviews do two things for your ecommerce store:
- They boosts SEO, adding primary and secondary keywords to your page in a natural, non-manipulative way.
- They serve as social proof—showing that real people have used the product and validating its performance across different use cases.
Generative engines look for detailed, experience-based feedback they can extract and summarize. The more specific the review, the stronger the signal.
Tip: Encourage reviews that speak to specific outcomes (“held up in heavy rain,” “perfect for studio apartments”). Where possible, also collect verified reviews on third-party platforms—these reinforce credibility from multiple angles and increase your chances of being surfaced by AI.
Add Q&A and use-case proof
Shoppers often ask AI real-world, intent-driven questions—and generative engines favor products that directly answer them.
Adding a Q&A section to your product page helps cover long-tail queries word-for-word, while showing that you’ve anticipated buyer needs.
Tip: Seed your Q&A with common concerns (e.g., “Will this chair fit under a desk?” or “Is the fabric scratch-resistant?”). Provide concise, benefit-focused answers. Use schema markup if possible to make these extractable by AI.
Earn mentions across trusted platforms
Products that get recommended consistently—on authoritative blogs, Reddit, review roundups—are more likely to be surfaced in AI-generated answers.
These mentions don’t just build authority—they create semantic associations, linking your brand to specific categories, use cases, and buyer intents.
If your mattress keeps showing up in eco-conscious product lists, or your backpack is frequently praised for durability, generative engines start associating those qualities with your brand—making it more likely to be recommended in similar queries.
Tip: Don’t just aim for backlinks like you would for SEO—aim for context. Partner up with publications, creators, and niche communities and proactively shape your product narrative.
How Generative Engines Recommend Products and How to Optimize
Now that we’ve covered how ecommerce brands can optimize for generative engines, here’s how major platforms like ChatGPT, Perplexity, Amazon Rufus, and Google AI Overviews actually surface product recommendations—and what data they rely on.
ChatGPT Shopping
ChatGPT Shopping tailors recommendations based on your query, past interactions and memories (if memory in turned on).
Where it gets its data:
- Structured metadata from third-party providers (e.g., price, product description) and other third-party content (e.g., reviews)
- Model responses generated by ChatGPT before it considers any new search results
- OpenAI safety standards
Optimizing for ChatGPT Shopping:
- Enrich and structure your product metadata—including schema markup, detailed attributes, pricing, and availability
- Write descriptions that reflect real-world intent, such as “noise-canceling earbuds for travel” or “warm winter jacket for rainy weather”
- Maintain consistency of data and content across all channels—from your website to marketplaces and resellers
- Build a strong digital footprint by encouraging customer reviews and seeding relevant conversations in forums
Learn more about ChatGPT Shopping here.
Perplexity Shop
Perplexity Shop ranks products based on authority and relevance. Merchants who provide product details are also more likely to be recommended.
Where it gets its data:
- Merchant feeds
- Shopify and BigCommerce feeds
- External reviews and forums
Optimizing for Perplexity Shop:
- Structure your product data cleanly, including clear specs, concise descriptions, and consistent formatting
- Use high-quality images and benefit-led summaries to help Perplexity generate scannable, AI-friendly cards
- Support visual discovery by labeling images and attributes consistently for features like “Snap to Shop”
- Join the Perplexity Merchant Program to appear in native product cards and enable “Buy with Pro” checkout
BigCommerce also recently announced their partnership with Perplexity that aims to ensure brands can send structured AI-friendly product data to Perplexity Shop. There’s no doubt about it — structured product data is critical for GEO.
Learn more about Perplexity Shop here.
Google Shopping
Google Shopping recommendations are driven by search intent, product data, and real-time context. The Shopping Graph processes billions of listings to match products to highly specific queries—factoring in things like attributes, seasonality, and even location-based trends.
Where it gets its data:
- Google Merchant Center feeds (data provided by merchants).
- Retailers' websites (via structured markup or crawling).
- User-generated content (reviews, ratings, and even videos).
Optimizing for Google Shopping:
- Enrich your Google Merchant Center feed with both required and optional attributes—GTINs, materials, colors, sizes, and detailed specs all help Google understand and rank your products
- Optimize product titles and descriptions using real shopper language and high-intent keywords (e.g., “hand-painted ceramic planter” instead of just “planter”)
- Use high-quality images and structured data like schema markup and image alt text to support visibility across Shopping, Search, and Image results
- Create educational content like product guides and blogs to influence AI summaries and drive traffic from informational queries
- Build brand visibility through reviews, mentions, and community engagement—get featured in listicles, affiliate blogs, and forums where Google pulls source material
Read more about Google Shopping here.
Amazon Rufus
Rufus is Amazon’s own generative shopping assistant. It operates entirely within Amazon’s ecosystem and only recommends products listed on Amazon—unlike the rest, which pull from multiple external sources.
Where it gets its data:
- Amazon’s internal product catalog (titles, bullets, specs)
- Customer reviews and Q&A
- External web
Optimizing for Amazon Rufus:
- Write detailed, structured bullets and titles that clearly explain key benefits and use cases
- Answer common customer questions in the Q&A section to match long-tail, intent-driven queries
- Encourage authentic customer reviews that mention specific use cases and outcomes
- Enhance A+ Content by using image alt tags and including text rather than solely images (Amazon Rufus can’t read text off images yet)
We tried out Amazon Rufus and honestly, it didn’t quite live up to expectations.
Conclusion
At the end of the day, generative engines aren’t trying to rank pages—they’re trying to answer questions. If your product content is clear, structured, and genuinely helpful, you’re already on the right track.
This isn’t about chasing hacks or gaming the system. It’s about giving AI the right signals so your products show up when it matters most. Structure your data, write like a human, and make sure you’re being talked about in the right places.
And if you need help ensuring complete, accurate, and well-structured data, or high-quality product descriptions, Hypotenuse AI can help. Reach out to us here.
