For Ecommerce

Make Your Products the AI's First Recommendation

When shoppers ask "what's the best [product] for me?", AI assistants pull from structured data, reviews, and brand authority to recommend a handful of stores. Ayzeo shows you whether yours is among them, who gets named instead, and which sources the engines read on the way to that answer.

Brand Industry Ranking of an online store project with the tracked shop ranked first and competitor shops listed with mentions, visibility, citations and average position

The shops AI actually recommends across your product prompts, with your store ranked among them.

See the Sources Behind Every Recommendation

AI assistants read review sites, communities and store pages before they recommend anything. The sources view shows which domains the engines consulted across your product prompts, how often your own pages were among them, and which third-party sites carry the most weight in your category.

  • Every consulted domain, classified as your site, brand sites, editorial or community
  • Your own share of the sources, measured per prompt
  • The review sites and communities worth your outreach effort
The AI Sources and Destinations table listing the domains AI engines consulted, with citations, prompts and share of answers per domain

The Challenge

Common problems ecommerce face with AI visibility

1

AI recommends competitor products, not yours

ChatGPT's shopping research feature now lets users describe exactly what they need -- "best waterproof jacket for Pacific Northwest winters" -- and returns curated product recommendations, while AI Overviews sit on top of a growing share of product queries. If your product pages lack structured data, comprehensive descriptions, and third-party validation, AI agents will recommend competitors who have them. And as agentic checkout protocols let AI agents complete purchases on behalf of users, the brand that gets recommended captures the transaction instantly.

2

Product structured data is incomplete or missing

AI agents don't experience your brand the way humans do. They can't respond to visual design, lifestyle photography, or carefully crafted UX. Instead, they evaluate machine-readable data: Product schema, Review markup, Offer pricing, availability, and breadcrumb navigation. Most websites implement little or none of it, which makes complete markup a real advantage. Incomplete or incorrect schema means AI literally cannot understand your products, prices, ratings, and stock status.

3

No way to measure AI-driven purchase intent or revenue

You track Google Shopping, paid social, and organic search with precision. But AI-driven product discovery is a growing blind spot, even as AI assistants send a fast-growing stream of high-intent visitors to stores. Without attribution, you cannot measure which products AI recommends, how many sessions originate from AI chatbots, or what revenue this channel generates.

How Ayzeo Helps

Purpose-built tools for ecommerce

Product Category Monitoring

Track prompts like "best running shoes under $150," "top wireless headphones for commuting," or "most reliable espresso machines 2026" across ChatGPT, Claude, Gemini, Perplexity, DeepSeek, and Grok. See whether your products appear, in what order they are mentioned, and which competitors are recommended alongside you. Monitoring these queries is how you understand your share of AI-mediated product discovery, and how you catch a competitor taking it.

Schema and Structured Data Audit

Validate your Product, Review, Offer, AggregateRating, and Breadcrumb schemas against current best practices. Ayzeo flags missing fields, incorrect markup, and opportunities to add JSON-LD that AI agents prioritize. Comprehensive structured data is the single most actionable technical lever for AI visibility -- it is what makes your catalog machine-readable for ChatGPT shopping research, Google AI Overviews, and the emerging agentic commerce protocols like UCP.

Brand Sentiment Tracking

Understand exactly how AI platforms describe your brand and products. Is the tone positive, neutral, or negative? Are product strengths highlighted or weaknesses amplified? Track sentiment changes over time and across different AI platforms. With AI now mediating the discovery layer before customers ever visit your site, brand perception in AI responses directly impacts consideration and conversion.

AI Traffic to Revenue Attribution

Connect Google Analytics to isolate AI chatbot traffic to your store. Measure sessions, engagement rates, and conversion by AI source. AI referral traffic converts at higher rates than Google search traffic. Attribute actual revenue to AI-driven visits so you can quantify ROI, justify budget, and optimize the product pages AI recommends most.

How It Works

From the first prompt to attributed revenue, the loop runs inside one project for your store.

1

Track your buyers' product prompts

The questions shoppers actually ask, per category: best-of, comparisons, use cases and alternatives.

2

See who gets recommended

Which shops and brands the engines name, in what order, and how your store ranks among them.

3

Fix what the engines read

Audit Product, Review, Offer and Breadcrumb schema and deploy the generated fixes, so AI can parse your catalog.

4

Measure the revenue

Connect Google Analytics to isolate AI assistant traffic and attribute sessions and conversions to the channel.

Frequently Asked Questions

Ready to Get Started?

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