An AEO audit is a structured review of how well your content is set up to be surfaced and cited by answer engines. You run one by baselining your current visibility across AI platforms, checking that your content is crawlable, reviewing whether your pages are answer-first and well-structured, and analyzing the gaps where competitors are cited and you are not. The output is a prioritized list of fixes.
In short
- An AEO audit measures how ready your content is to be cited in AI answers.
- The core steps are baseline, technical check, content review, and competitor gap analysis.
- It starts from a defined prompt set, the questions your audience actually asks.
- The output is a prioritized list of fixes, not just a score.
What is an AEO audit?
An AEO audit is an assessment of your content's readiness to win citations in answer engines, combined with a measurement of where you currently stand. It looks at both the technical foundation, can engines reach your content, and the content itself, is it structured to be extracted as an answer, and compares your visibility against competitors.
It differs from a traditional SEO audit in focus. An SEO audit centers on rankings, links, and technical health for search engines. An AEO audit keeps the technical foundation but adds the questions specific to answer engines: are your pages answer-first, are they chunked into extractable sections, are you cited in AI answers for your priority questions, and where are competitors winning instead. The goal is to turn that assessment into action, which is why it ends in a prioritized plan rather than just a report. The broader measurement discipline behind it is covered in how to measure AI visibility.
Step 1: Baseline your current AI visibility
The first step is to establish where you stand by defining the questions that matter and testing them across answer engines. Without a baseline, you cannot tell whether anything you change later is working.
Start by building a prompt set: the real questions your audience asks across the buying journey, from definitional and category questions to comparison and problem questions. Then run those prompts across the engines that matter to you, ChatGPT, Claude, Gemini, Grok, Perplexity, Copilot, and Google's AI Overviews and AI Mode, and record whether your brand is mentioned, whether your pages are cited, how you rank among mentions, and how you are described. Because answers vary between runs, read the pattern across many prompts rather than any single response. Building a prompt set well is covered in how to measure AI visibility, and a tracking tool automates this baseline so you can repeat it on a cadence.
Step 2: Check the technical foundation
The second step is to confirm that answer engines can actually reach and read your content, because the best content is invisible if crawlers cannot access it. This is the prerequisite that everything else depends on.
Check a few things. Confirm your important content appears in the initial HTML rather than being rendered only by JavaScript, since many AI crawlers do not execute JavaScript and will see an empty page. Confirm that AI crawlers are allowed rather than blocked in robots.txt. Verify that pages are reachable, indexable, and reasonably fast. And confirm your content ranks, since answer engines that search the web draw heavily on pages that already rank well. These technical foundations are covered in depth in LLM optimization, and any failures here are usually the highest-priority fixes, because they gate everything else.
Step 3: Review content extractability
The third step is to review whether your priority pages are written and structured to be extracted as answers. This is where most AEO-specific opportunities are found.
For each important page, assess a few qualities. Is it answer-first, leading each section with a direct answer, covered in answer-first content? Is it chunked into self-contained sections under question-led headings, covered in how to structure a page to get cited by AI? Does it use the right format for each question, paragraph, list, or table? Does it support answers with evidence, statistics, quotations, and citations? And is it current, since freshness is a measured factor? Pages that fail these checks are the ones to rewrite, and the fixes tend to be straightforward to apply.
Step 4: Analyze competitor and citation gaps
The fourth step is to find the specific questions where competitors are cited and you are not, and to understand why. This turns the audit from a general review into a targeted plan.
For your priority questions where you are absent, look at who is cited instead, which of their pages or sources the engine uses, and what type of content wins, a comparison page, a listicle, a community thread, a piece of original data. That tells you what to build or improve to compete for each question. Patterns across these gaps, a competitor consistently winning with comparison pages, or community sources dominating a set of questions, point to where to focus. Using citation source data to find these targets is part of the measurement discipline, and it feeds directly into a prioritized roadmap of fixes ranked by value and effort.