AEO frequently asked questions

answer engine optimization (AEO, sometimes GEO — generative engine optimization) is the practice of making sure your brand gets named when an AI assistant like ChatGPT, Gemini, or Perplexity answers a buyer's question. it differs from SEO because the surface is the AI's one-paragraph answer, not a results page of links. the metric is whether you got cited, not whether you ranked.

yes, in practice. AEO (answer engine optimization) and GEO (generative engine optimization) are interchangeable terms for the same practice — getting cited by AI assistants when they answer buyer questions. GEO is becoming the more common term in 2026; AEO was more common in early 2025.

SEO optimizes for blue-link rank on Google. AEO optimizes for being named in the AI's answer paragraph. SEO signals (backlinks, technical, keywords) still matter but with different weights. AEO leans harder on prose-density of the page, structured comparison content, first-party data, and citations on editorial sources the AI grounds in. you can rank #1 on Google and be absent from ChatGPT for the same query.

the three with material grounded-search market share in 2026: ChatGPT (OpenAI's live web-search-augmented model), Perplexity (always grounded with explicit citations), and Gemini (Google's grounding index, which also powers AI Overviews in classic Google Search). they pull from heavily overlapping but distinct document sets — so disagreements between them are the actionable signal.

it measures the percentage of buyer-intent queries an AI assistant cited your brand in. if you score 60/100, your brand was named in 3 of 5 buyer queries the audit tested. 100/100 means you got cited in every query, 0 means you are invisible in this category. the queries are buyer-intent (what a real buyer would type to an AI), not brand-name lookups.

an LLM infers your tight product category from your domain, identifies the top competitors a buyer would consider, and generates five real questions a buyer evaluating that category would ask an AI assistant. queries are budget-aware, use-case-aware, and never include the brand's own name — that would just produce trivially positive results.

if you ask an AI "is acme a good DTC coffee brand", it will almost always say yes — it's primed by the brand name. that measures nothing about real visibility. buyer-intent queries simulate the actual moment of purchase: the buyer doesn't know your brand yet, they ask the AI which brand they should pick. that's the moment your visibility score determines whether you exist for that buyer.

yes — go to ChatGPT, Gemini, and Perplexity, ask each one a real buyer question from your category ("best running shoes under $150"), and check if your brand is in the answer. do this for 5 queries × 3 engines = 15 lookups per audit. tools just automate it, store history, and let you track week-over-week movement. for one-off spot checks, manual is fine.

weekly is enough for most brands. AI grounding indexes refresh faster than Google's main index did in the 2010s — a fix you ship on Monday can change citations by Friday. paid plans typically rerun daily and email a weekly delta. monthly is too slow if you're actively shipping content.

modestly. structured data helps when a query has a clear schema match (recipes, products, FAQs). for most buyer-intent queries, prose density and concrete first-party data matter more than schema. don't skip schema — it's cheap — but don't expect it to move citations on its own.

yes, with different weighting. high-quality editorial citations on domains the AI already trusts for your category move the needle the most. random link-farm backlinks that worked for classical SEO have near-zero AEO impact. one Wirecutter mention beats a hundred low-quality directory links.

a lot. AI assistants weight prose-density of "real humans recommending things to each other" higher than the link graph ever did. a strong Reddit thread or Stack Exchange answer that names your brand alongside your use case can move citations more than a tier-2 editorial review.

single-purpose pages that answer one buyer-intent question better than any other page on the web. 600 to 1200 words. clear question in the H1. answer in the first paragraph. first-party data (your own benchmarks, customer quotes, comparison tables). this is the original web idea — "one good page on one topic" — that SEO drove into the ground.

10,000-word "ultimate guide" pieces with 60% keyword filler. listicles that pad every entry with the same 200-word boilerplate. AI assistants strip these down to one or two sentences and skip the fluff entirely — meaning the brand gets less surface area in the answer than a shorter, denser page would have given them.

no, not reliably. AI assistants strip or spoof referrer headers, so most of the citation-driven traffic shows up as direct or branded search rather than "from ChatGPT". anyone claiming to measure "AI search traffic" with high confidence is mostly guessing. measure citation rate (input metric), not traffic from AI (output metric).

12-16 weeks from your first published page to measurable category-level visibility, assuming consistent shipping of single-purpose pages plus earned citations. faster than classical SEO (which used to take 6-12 months) because AI grounding indexes refresh more often. one query at a time can move within a week if you ship the right page.

partially. Google AI Overviews use Gemini's grounding layer, which is the same retrieval used by Gemini API with Google Search grounding. ChatGPT's web search uses a different mix (Bing-derived + some private retrieval). Perplexity grounds in its own scraping pipeline. so AI Overviews ≈ Gemini, but ≠ ChatGPT and ≠ Perplexity. you need all three to know whether your visibility is uniform.

this is more common than you think — being invisible at zero is the starting point for most brands in 2026. the playbook: identify the 5 queries you're losing, ship one single-purpose page per query (your blog), pitch your brand to the 2-3 editorial domains the AI assistants are already grounding on for those queries, and re-run weekly. expect first citation within 30 days, meaningful score within 60.

expand the test set. run the audit on a wider set of high-intent queries you'd lose first if you slipped. score 100 on 5 queries is great; it doesn't tell you about the next 50 queries adjacent buyers might ask. also: watch the week-over-week delta. holding 100 is harder than getting to 100, because competitors who don't have a score 100 are actively trying to take yours.

no — it overlays it. classical SEO traffic is still meaningful in 2026 (especially for transactional queries where buyers want to click through to compare prices and reviews). AEO captures the informational and recommendation phases that AI assistants have absorbed. teams that are winning are running both — and finding that AEO-shaped content also performs well on classical SEO, because the qualities that win AEO (concrete answers, first-party data, single-purpose pages) also win modern Google.