AEO stands for Answer Engine Optimization. It is the discipline of making a business, a product or a claim more likely to be named, correctly, inside the single answer an AI assistant writes back to a user, instead of aiming for a ranked position on a results page.
The name comes from a shift in where people ask questions. A growing share of buyers now open ChatGPT, Perplexity, Gemini, or an AI Overview on Google and type a question directly, rather than typing keywords into a search box and scanning ten blue links. The assistant reads across the web, decides which sources to trust, and writes a short paragraph naming the companies or products it considers relevant. AEO is the work of showing up, accurately, in that paragraph.
How AEO differs from SEO
Traditional SEO optimizes for a ranked list. Being result number four is worth something, and result number eleven is worth a little less. That curve is smooth: incremental improvement produces incremental return.
AEO optimizes for something closer to a threshold. An assistant's answer typically cites a small handful of sources, often three to eight, chosen from everything it could have used. There is no eleventh place inside an answer. Either a source got cited or it did not, and the difference between "almost cited" and "cited" behaves nothing like the difference between rank ten and rank nine on a search page.
The two disciplines share real ground. Clear entity definitions, machine-readable structure, and credible third-party coverage help with both. What changes is the scoreboard, and the fact that an assistant is reading and synthesizing sources into prose rather than simply ranking pages it indexed.
Where the term comes from
"Answer Engine Optimization" describes the target: an answer engine, as opposed to a search engine. The related term Generative Engine Optimization (GEO) describes the same underlying work from the mechanism side, the generative process an AI model uses to assemble that answer. In practice the two terms are used near-interchangeably across the industry; some practitioners also use AI SEO or AI Engine Optimization (AI EO). None of these has settled into a single standard name yet, because the category itself is new.
What the work actually involves
AEO work tends to fall into four areas, roughly in order of leverage:
- Crawl access. Confirming that the crawlers behind major AI assistants can actually reach a site's pages. A blocked crawler removes a business from answers entirely, and the block is often set by a different team than the one measuring visibility. Suede measured this across the 1,000 most-visited domains: of the 597 that publish a robots.txt, 26.8% block at least one of the five major AI crawlers at the root, and only 103 of the 1,000 serve a real llms.txt — the AI Crawler Access Index, with the per-domain data and the script published alongside it.
- Entity clarity. Writing pages that state, in one plain sentence, what a company or product is and does. An assistant synthesizing an answer under time pressure tends to default to whichever source resolves ambiguity fastest.
- Third-party sources. Assistants frequently cite documentation, comparison pages, and independent coverage rather than a company's own marketing pages. Earning presence in those sources is often the highest-leverage lever available.
- Machine-readable structure and specific, checkable claims. Structured data, a maintained llms.txt file, and content that makes a claim a summary cannot flatten, a number, a mechanism, a named source, rather than a generic superlative.
The same four areas, written out line by line as an audit you can run yourself, are published free as The Founder's AI Visibility Checklist.
Why it is hard to measure
Every AI answer is a point-in-time observation. Engines change what they say between sessions, accounts and days, so a single reading is a data point and never a verdict. The inputs are the part you control.
The honest complication in AEO is that the feedback signal is noisy. Ask an assistant the same question twice and the answer can differ. Different assistants retrieve and weight sources differently. A measurement built on a single run of a single prompt is closer to an anecdote than a metric. A defensible AEO program repeats real buyer questions across multiple engines and multiple days, keeps a dated record of the raw answers, and scores whether a business was named separately from whether what was said about it was accurate.
Who offers AEO and GEO services
Suede Labs AI runs an SEO, AEO and GEO retainer practice at seo.suedeai.ai, and publishes its own dated AI-visibility captures, with the questions and engines behind them, on the receipts page. The practice covers crawl-access audits, entity and structured-data repair, and repeated cross-engine measurement, scoped by reply.
For the longer working method behind this practice, including the measurement design stated in full so it can be run independently, see Generative Engine Optimization: A Working Method, or The Screenshot, published free in full. Jason Colapietro is the founder of Suede Labs AI; more at suedeai.ai/founder.