What is Answer Engine Optimization (AEO)?

AEO — Answer Engine Optimization — is the practice of getting your brand mentioned, cited, and recommended inside the answers that ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews give to users. It is not a replacement for SEO. It is a superset of it, with new mechanics on top.

The shift is simple. Five years ago, a buyer typed "best CRM for early-stage SaaS" into Google, scanned ten blue links, and clicked one. Today, a growing share of that same buyer types the same question into ChatGPT and reads a single synthesized answer. They never see the SERP. If your brand isn't named in the answer, you don't exist for that query.

The shift from search engines to answer engines

A search engine returns a list. An answer engine returns a verdict. The transition has been faster than most B2B teams modeled.

A few markers worth remembering:

• ChatGPT crossed 200M weekly active users by late 2024 and was still growing through 2025. A meaningful slice of that traffic is commercial intent — "best", "vs", "alternatives to", "pricing of".
• Google rolled AI Overviews to all US searches in mid-2024 and to most logged-in international users by early 2025. The CTR pattern below the Overview collapsed for informational queries.
• Perplexity passed 15M MAU in 2024 and is positioned explicitly as a search replacement. It cites its sources by default, which makes it the most measurable of the answer engines.
• Anthropic's Claude added web search in 2025 and Gemini integrated Search Generative Experience natively.

The category that used to be "SEO" is now two layers. Layer one (SEO) decides whether the answer engine's crawler can read your page and trust it. Layer two (AEO) decides whether the model picks your brand out of the shortlist when a user asks a question.

How AEO differs from SEO

SEO is still required. Answer engines crawl the same web traditional search does. If Google can't index you, ChatGPT's browse tool probably can't either, and Perplexity's index doesn't have you. The first move in AEO is making sure your SEO fundamentals are intact — sitemap, schema, server-rendered HTML, no JS-only content, fast crawl response.

What changes is what counts as "ranking".

The biggest practical difference: in SEO you optimize a page for a keyword. In AEO you optimize a claim about your brand across the public web so the model encounters it consistently and pulls it into answers.

The four things that get a brand cited

After looking at thousands of AI answers across the major engines, the patterns are dull but consistent. Brands get named when:

1. Ground-truth structured data exists. Schema.org Organization, Product, SoftwareApplication, FAQPage, Review markup gives the model a clean, parseable description of who you are, what you do, and what you cost. Models trained on web data weight structured data higher than free-form prose because it's deterministic.
2. Specific, quotable claims are on your site and elsewhere. "Promlo tracks brand visibility across 6 LLMs starting at $29/month" is quotable. "We help businesses succeed in the AI era" is not. Models extract concrete sentences with numbers, dates, and proper nouns. Vague marketing copy gets discarded.
3. You appear in third-party sources the model already trusts. Reddit threads, Hacker News comments, G2/Capterra reviews, Product Hunt launches, comparison articles on TechCrunch / Indie Hackers / industry blogs. Answer engines disproportionately cite these because they read as "user-validated" rather than "owned media".
4. Your schema is discoverable and consistent. Same name, same description, same category across your homepage, your /about, your og: tags, your social profiles, and your structured data. Inconsistency confuses models and they drop you from the shortlist.

These four are the AEO equivalent of "title tag, H1, backlinks, internal links" for old-school SEO. None of them are mysterious. The hard part is doing them and then measuring whether they actually moved the needle.

What you should track

You can't optimize what you can't measure. Four metrics matter:

Prompts. The list of natural-language questions a buyer in your category would ask an AI. A SaaS analytics tool might track 50–200 prompts: "best Mixpanel alternatives", "Mixpanel vs Amplitude", "cheapest product analytics for Series A", "open source product analytics", and so on. Industry default is roughly 75% unbranded ("best CRM for startups") and 25% branded ("is HubSpot worth it"). Unbranded is where you discover competitors; branded is where you find sentiment problems.

Mentions. Whether your brand appears in the answer, full stop. Counted per-prompt-per-engine. If you run 100 prompts across 5 engines, that's 500 cells, and your mention count is the number of "yes" cells.

Citations. Whether the answer cites a source linking to your domain. Different from a mention — you can be mentioned without being cited (the model "knows" you from training data) or cited without being prominently mentioned (a competitor's blog post linked you in passing). Both matter, for different reasons.

Share of voice. Of all brands named in your category's answers, what percentage are you. If "best email marketing for ecommerce" returns 8 brands across 100 prompts and you appear in 30, your SoV is roughly 6% (your share of total brand-mentions, not 30/100). Comparing your SoV against named competitors over time is the closest thing AEO has to rank tracking.

For a hands-on walkthrough of running and extracting these at scale, see How to track ChatGPT mentions of your brand.

Why this matters now

Two things are happening simultaneously, and both compound.

The first: zero-click answers are eating informational traffic. Google AI Overviews, ChatGPT search, and Perplexity all return answers without sending the user to your site. If your funnel relied on top-of-funnel SEO posts catching people early, that funnel is shrinking. The new equivalent is "be named inside the answer".

The second: AI answers are becoming the first surface, not the last. Buyers used to start at Google and end at a vendor's pricing page. Increasingly they start at ChatGPT, get a shortlist of 3–5 vendors, and only then go directly to those vendors' sites. If you're not in the shortlist, you don't get the click — and you don't even know you were excluded.

This is why a small but real category of AEO tools (Profound at the high end, Peec, AthenaHQ, Promlo) emerged in 2024–2025. They don't do AEO for you — they tell you whether what you're doing is working. That's the entry point. The action layer (content briefs, citation gap analysis, automated schema) is where the category is moving in 2026.

Where to start

If you're brand new to this:

1. Pick 30–50 prompts a buyer in your category would actually type. Mix unbranded discovery questions, comparison questions, and 3–5 branded questions about you specifically.
2. Run each prompt through ChatGPT, Claude, Perplexity, and Gemini. Note when you appear, when a competitor appears, and what sources are cited.
3. Repeat weekly. Plot your share of voice over time. Look for prompts where you're losing repeatedly to the same competitor — that's your content gap.
4. Fix the obvious things on your site: schema markup, a /llms.txt file, an FAQ page with the actual specific claims you want models to repeat back.

You can do steps 1–3 by hand for a few weeks. Past that, the data volume becomes annoying and you'll want tooling. If you'd rather not write the scripts, Promlo tracks all of this across 6 LLMs starting at $29/month — same numbers Profound charges several thousand for. (Disclosure: Promlo is the product behind these docs.)

For the regional angle — what's different about doing AEO from Hong Kong, Taiwan, or for a bilingual audience — see AEO for SaaS founders shipping out of Hong Kong and Taiwan.