What is answer engine optimization (AEO)?

SEO optimizes for a crawler that indexes pages and a ranking algorithm that orders them. AEO optimizes for a model that reads pages, synthesizes an answer, and decides — per question, per run — which brands to name and which sources to cite. Three practical differences follow.

First, the metrics change. Rankings and click-through rate stop being observable; what you can measure instead is mention rate, share of AI voice against competitors, average rank within the answer, recommendation rate, and citation share. We define each of these in How to measure brand visibility in AI answers.

Second, the evidence pool replaces the index. A grounded engine searches the live web, pulls a handful of sources into context, and answers from those. If your domain is never in that handful, you are invisible for structural reasons no amount of on-page polish fixes — which is why checking crawler access and citations comes before content work.

Third, the results are probabilistic. Ask the same engine the same question three times and you can get three different answers. Any AEO number computed from a single run is noise wearing a percentage sign. Our methodology samples every question three times per engine and stores every answer, so each figure is a dated snapshot you can audit — the full detail is on the methodology page.

Gartner's February 2024 prediction is the number the industry runs on: "By 2026, traditional search engine volume will drop 25%, with search marketing losing market share to AI chatbots and other virtual agents" (Gartner press release, 2024-02-19). We quote it verbatim because it is a prediction, not a measurement — but 2026 is now, and the buyers writing to us describe the mechanism directly: customers arrive saying an AI assistant recommended a competitor.

For a DTC or e-commerce brand the exposure is concrete: shopping questions — "best X for Y", "is brand A legit", "alternatives to B" — are exactly the question shapes assistants answer in full, with product names. When the engine names three brands and yours is not one of them, the buyer's shortlist formed without you. What we can measure, brand by brand, is how often that is happening on your actual buyer questions — that is what the audit does.

We publish our own numbers because we ask you to trust ours. On 2026-08-23 we ran the full AEOSearch audit on AEOSearch itself — 300 stored, grounded answers across ChatGPT, Claude, Gemini, Grok and Perplexity. Our unprompted mention rate was 0%. Our citation share was 0%. That is the honest starting line for a young brand, and every method on this page is what we are running on ourselves, in public, to move it.

Not with content. It starts with a measurement: which questions do your buyers ask, and what do the engines answer today? From a stored baseline, three workstreams follow in order — fix the evidence-pool prerequisites (crawler access, schema, llms.txt, citations), close the content gaps behind the questions you lose, and re-measure on a schedule so movement is real, not remembered.

The mistake we see most is skipping the baseline and shipping "AI-optimized" content into the void. Without a stored before-and-after, nobody can say whether anything moved. Start with the free scorecard — 8 questions, real engines, no invented numbers — or read how an answer engine picks a winner first.