AEO for Agencies: How to Audit and Improve Your Clients' AI Visibility
Agencies are fielding more client questions about AI search. This guide covers how to structure an AEO audit, what to look for, and how to report findings clients can act on.
Clients are starting to ask about AI search visibility. Some know the term AEO. Others just notice their competitors showing up when they ask ChatGPT for vendor recommendations and want to know why.
AEO audits follow a repeatable process. The diagnostic work maps to things agencies already do: competitive analysis, content audits, structured reporting. The difference is the signal types and where to find them.
Why this is landing on agencies now
Enterprise and mid-market buyers increasingly start vendor research by asking AI engines. A brand absent from those answers is invisible before the buyer has even formed a shortlist.
Clients who understand this are asking agencies to help. Clients who don't will, as soon as they notice a competitor appearing in AI answers and they don't. Building an AEO capability now positions your agency to answer that question before a competitor does.
The three parts of an AEO audit
A solid AEO audit has three layers: a query audit, a source audit, and a content audit.
The query audit determines whether the client appears in AI answers and for which queries. Start by building a set of 20 to 30 queries across four types: category queries ("best [product category] for [use case]"), problem queries ("how do I solve [pain point]"), comparison queries ("[client] vs [competitor]"), and feature queries ("which tools support [feature]"). AEO keyword research covers how to build this query set methodically.
Run each query in ChatGPT, Perplexity, and Gemini. Record whether the client appears, which competitors appear, and which sources the engine cites. A shared spreadsheet with this data is already a deliverable.
The source audit maps where the client's brand signal comes from and where it's missing. For each AI answer, check the cited sources: are they review platforms, press coverage, comparison roundups, or the client's own site? Then check the sources you'd expect to matter for the client's category, and compare their presence to competitors who are already appearing.
The content audit reviews the client's own site for extractability. AI engines cite content that directly answers a question without requiring context from elsewhere on the page. Marketing copy rarely qualifies. FAQ pages, definition posts, and comparison pages do. Content formats AI engines prefer is a reference worth sharing with clients at this stage.
What each engine reveals
ChatGPT, Perplexity, and Gemini weight different signal types. Running one query in one engine gives you a partial picture.
ChatGPT relies heavily on training data, so its knowledge of a brand can lag months behind. It favors high-authority domains: major review platforms, well-known publications, Wikipedia. Clients with accurate, detailed coverage on G2 and in notable press tend to appear here.
Perplexity behaves more like a live web search. It cites specific URLs and refreshes more often. Brands in current roundup articles with active review profiles tend to outperform brands relying on older press.
Gemini integrates with Google's index, so it tracks closely with what ranks well in Google Search. A brand with strong organic rankings often translates to stronger Gemini visibility.
Map which engines each client appears in and which they don't. The pattern tells you which signal types to prioritize.
Gaps you'll find repeatedly
After running a few audits, certain patterns appear across clients.
Missing from comparison and alternative queries. Clients often have decent category visibility but disappear when a buyer asks "[client] vs [competitor]" or "alternatives to [competitor]." These high-intent queries are where visibility matters most. How AI engines handle brand comparisons explains the signals that drive these answers.
Thin third-party presence. A client may have strong on-site content but almost no presence on review platforms, communities, or trade press that AI engines treat as independent verification. Without third-party corroboration, even excellent own-site content gets limited citation weight.
Stale or inaccurate descriptions. AI engines sometimes describe a brand based on outdated sources: a press release from two years ago, a G2 profile that hasn't been updated, an old blog post with an outdated positioning. The engine's description of the client may not match what the client actually does now.
Absent from problem-oriented queries. Most clients publish content around product categories and features, not around the problems buyers have before they know the product category exists. If no one has written content matching those pre-awareness queries and linked it to the client's solution, the client won't appear when a buyer asks.
How to package AEO work
AEO deliverables map to familiar agency formats.
An AEO visibility report is a snapshot: where the client appears today, where competitors appear, which sources drive each answer. It reads like a competitive analysis, which most clients understand immediately.
An AEO roadmap prioritizes the gap-closing work: which external sources to build, which content to create, which existing pages to rewrite for extractability. Prioritize by query importance and estimated effort.
An AEO monitoring retainer runs the query set monthly, tracks whether visibility is improving, and flags new competitor appearances or source changes. This is a natural recurring engagement and avoids dependence on project work alone.
Reporting to clients
Keep reports grounded in specific queries, not abstract metrics. "You appeared in 4 of 20 queries in ChatGPT this month, up from 2 last month" is concrete. "AI visibility improved" is not.
Show the engine's actual text when reporting how the client is described. Clients respond more to seeing their brand described inaccurately or incompletely than to any audit chart. The raw AI output is often the most compelling part of the report.
Tie query selection to buyer intent, not general brand awareness. A client cares about appearing when buyers are making decisions. A query like "best inventory software for distributors" is more meaningful to them than "what is [brand name]."
QuickAEO lets agencies run structured query sets across ChatGPT, Perplexity, and Gemini and pull results into shareable reports. It handles the query-running and citation tracking that would otherwise take hours of manual work per client.