Direct answer: Answer Engine Optimisation (AEO) helps a Shopify store answer specific buyer questions clearly. Generative Engine Optimisation (GEO) helps AI systems interpret and, where appropriate, cite reliable merchant facts. Both rely on accurate product data, policies and evidence. Neither guarantees a citation, traffic or sales.
What is AEO, and how is GEO different?
AEO focuses on making a question and its answer easy to find and understand. GEO also considers how generative systems synthesize information from the store and other sources. Standard SEO still matters: crawlable pages, useful content, sensible titles and internal links help people and machines find the same source of truth. These are practical content and information practices within Machine Experience, not separate Codified Commerce products.
Start with the buyer's question
Collect actual questions from store search, support, product reviews and pre-purchase conversations. For each question, identify the best canonical destination: a product page for product-specific facts, a policy page for terms, a collection page for selection guidance or an article for a broader explanation. Put a concise answer close to its heading, then explain conditions and exceptions.
For example, a buyer asking whether a product fits a particular device needs the exact compatibility evidence, supported models and limitations. A generic claim such as “works with all devices” creates avoidable risk for both a human buyer and an AI assistant.
Build a reliable Shopify information base
- Product truth: maintain clear titles, variants, attributes, current prices and availability.
- Buying conditions: keep delivery, returns, warranty and eligibility language consistent across pages and checkout.
- Page structure: use descriptive headings, specific answers and internal links with a clear destination.
- Structured data: represent visible facts accurately; markup alone cannot repair missing or contradictory information.
- Evidence: identify the source and date for claims that can change, and avoid invented customer results.
Why structured data alone is not Machine Experience explains the distinction between markup and a trustworthy commerce environment.
How do the three CAT AI frameworks support this work?
Machine Experience checks whether people and AI systems can interpret the store's products, policies and buying actions. Moving Target observes meaningful changes in customer questions, search and AI visibility. Next Product Move helps determine whether a recurring gap calls for better information, a product change or a different offer. CAT AI runs a feasible Automation from the merchant's business goal with expert management where needed.
How should you measure AEO or GEO?
Record a baseline of relevant pages, questions, search impressions and visits, qualified enquiries, and orders before changing content. Keep a dated sample of AI answers for the same questions and location where testing is available. After a change, compare like-for-like periods and note other causes: ranking shifts, seasonality, advertising, inventory and platform changes. A citation or AI referral is evidence of visibility, not proof that the content change caused a sale.
We do not present generic case-study percentages as Codified Commerce results. A merchant-specific outcome needs its own baseline, measurement window and attribution limits.
Frequently asked questions
Will adding FAQs make ChatGPT recommend my products?
No. FAQs can make useful facts easier to interpret, but an AI platform determines its own retrieval and recommendations. The product must also fit the buyer's request and satisfy any channel requirements.
Should AEO and GEO each have a separate service package?
The appropriate action depends on the store. CAT AI may address product facts, content, policy consistency or measurement within a Shopify Automation; there is no universal checklist that guarantees placement.
