What is AEO and how is it different from traditional SEO?
Answer Engine Optimization (AEO), sometimes called Generative Engine Optimization (GEO), is the practice of structuring a website so AI systems can find, understand, and cite it when a user asks a related question in ChatGPT, Perplexity, or a Google AI Overview. Traditional SEO optimizes for ranking in a list of blue links; AEO optimizes for being the one paragraph a model actually quotes or paraphrases as its answer. The two overlap heavily (both need crawlable pages, genuine content, and authority signals), but AEO puts more weight on structured markup and self-contained, question-shaped content than on backlink volume alone.
How do ChatGPT, Perplexity, and Google AI Overviews decide what to cite?
Each engine crawls and indexes the web on its own schedule (Perplexity leans on live web search, ChatGPT's browsing tools fetch pages directly, Google AI Overviews draws on Google's existing index), then looks for content that answers the query in a self-contained way, structured data that removes ambiguity about who you are and what you offer, and corroboration: does more than one credible source say the same thing about your business. Industry research has found that only about 12% of URLs cited by AI systems overlap with Google's own top-10 results for the same query, which is a strong signal that AEO and classic SEO are not the same game played on two boards.
What is llms.txt and do you need one?
llms.txt is an emerging, plain-text convention, similar in spirit to robots.txt, that gives AI crawlers a direct, structured briefing on your business: what you do, who it's for, pricing, and how to get in touch, instead of forcing the model to infer that from marketing copy and navigation menus. We publish our own at igdigi.com/llms.txt as a live example. It won't replace good content, but it removes a layer of guesswork for any agent reading your site.
What schema markup actually moves the needle?
Not every schema type carries equal weight. The ones worth prioritizing:
- Organization: your name, official profiles (sameAs), and contact details, so a model can confirm you're a real, identifiable entity.
- Service: a clear description of each offer, with pricing where you're willing to publish it.
- FAQPage: question-and-answer pairs that must match your visible text word for word. Mismatched schema is a trust signal working against you, not for you.
- Article: headline, author, and publish date for long-form content pages like this one.
- BreadcrumbList: reinforces how your site is structured, which helps a crawler understand context around a page.
How does content structure affect whether an AI model cites you?
Front-load the direct answer in the first sentence or two, before any "in this article we'll cover" framing: models tend to extract from the top of a page first. Phrase your subheadings as the actual questions people type into a chat box, not marketing headlines. Keep each answer self-contained under its own heading, so a model can quote one paragraph without needing the rest of the page for context. And where a comparison is genuinely useful, use an actual table: structured rows and columns parse cleanly for a model in a way that a wall of prose doesn't.
Do mentions elsewhere on the web matter?
Yes, this is the entity-consistency piece. Keeping your business name, address, and phone number identical across your site, your Google Business Profile, and relevant directories helps a model corroborate that you're a real, singular entity rather than three loosely related listings. Being named on other credible, topic-relevant pages, like an industry page such as our own personal injury law page, reinforces the same signal from a second direction.
How long does it take, and how do you measure it?
There's no dashboard toggle for AI citations the way there is for a paid ad. Technical implementation, schema, llms.txt, and content restructuring, typically completes in 4 to 8 weeks. After that, the honest way to measure progress is to directly ask ChatGPT and Perplexity what they know about your business, on a recurring schedule, and track whether the answer gets more accurate and more detailed over time. That's why our AI search visibility service includes monthly citation tracking rather than a one-time report.
SEO vs. AEO at a glance
| Dimension | Traditional SEO | AEO |
|---|---|---|
| Goal | Rank in a list of links | Be the quoted or paraphrased answer |
| Primary signal | Backlinks, keyword relevance | Structured data, entity consistency, self-contained answers |
| Content format | Long-form, keyword-targeted pages | Question-shaped headings, front-loaded direct answers |
| Machine-readable layer | Meta tags, basic schema | Schema.org (FAQPage, Article), llms.txt |
| Where it shows up | Google search results page | ChatGPT, Perplexity, Google AI Overviews |
The two aren't competing budgets, they're complementary. A page built for AEO (clear structure, real schema, direct answers) tends to perform better in classic search too, since it's simply better organized content. The same discipline that makes a phone line convert better, see how we think about that for AI voice agents, applies here: answer the actual question, fast, and structure it for machines to trust.