AEO, explained for store owners.

Answer Engine Optimization (AEO) is the practice of getting your business named when an AI assistant answers a buyer’s question. When someone asks ChatGPT, Perplexity or Claude “what’s the best X?”, the assistant recommends a short list of specific brands. AEO is the work of becoming one of those names. It’s also called GEO (Generative Engine Optimization). The labels overlap, but their usage varies; Citano uses “AI-search visibility” when plain language is clearer.

Why it’s different from SEO

Traditional search shows a page of ten blue links and the shopper chooses. An answer engine returns one synthesized answer naming a few brands — and the shopper often never sees a competitor. If you’re not in that short list, you’re invisible, not just ranked lower.

How AI actually decides who to recommend

Getting named is two different problems with two different levers — and confusing them is the most common mistake in this space.

• The owned game: make the useful facts on your own site easy to find, understand and verify. Google says unique, first-hand, well-organized content and a clear technical structure remain the foundation for Search and its generative features. The original GEO benchmark found that evidence-led interventions could improve visibility in its tested conditions, but those results are not a universal ranking formula.
• The earned game: build a real reputation beyond your own domain. Search and answer products can retrieve independent reviews, forums and publications. Relevant, authentic coverage helps buyers verify a claim; manufactured mentions are not a durable tactic.

For shopping experiences, keep product identity, availability, price and quality information current. OpenAI and Google both document these commerce-data foundations; neither publishes a guaranteed weighting formula.

What doesn’t work (the snake oil)

Google explicitly says there is no special AI file, schema or mandatory content format for its generative Search features. llms.txt is not a Google visibility tactic, while supported structured data remains useful for rich-result eligibility when it matches visible content. We refuse to sell either as a guaranteed citation shortcut, and the full guide shows the practical steps and evidence boundaries.

Who needs AEO?

Any business a customer might ask an AI assistant to recommend — most sharply, online stores. AI shopping (ChatGPT Shopping and Perplexity) is becoming a real discovery channel. If a store is absent from a relevant answer, it receives no opportunity from that particular interaction. That makes AI-search visibility worth measuring rather than assuming, especially for stores already investing in accurate product data and useful pages.

How to start: measure first

You can’t fix what you can’t see. The first step is finding the exact buyer questions where AI names a competitor instead of you, per engine — then rewriting your key pages with real evidence, publishing honest “you vs [competitor]” comparisons, and earning mentions on the sources AI already cites. That’s the loop Citano runs for online stores.

Keep reading

• How to get your business recommended by ChatGPT — the practical, evidence-based playbook
• How to get your Shopify store recommended by AI — the Shopify-specific version
• Citano AI visibility tracker — what we measure and how the workflow works
• Citano vs Profound · vs Otterly.AI · vs Vizby — how the tools differ
• Citano vs llms.txt apps — why the cheap tactics don’t work

Sources

• Aggarwal et al., GEO: Generative Engine Optimization (KDD 2024) — arxiv.org/abs/2311.09735
• Google Search Central — generative AI optimization guidance — developers.google.com
• OpenAI — publisher, crawler and referral guidance — help.openai.com