If your customers are starting conversations with ChatGPT, Gemini, Claude, Perplexity, or other AI assistants before they ever visit Google, there is a new marketing question worth asking:
What does AI say about my brand when my customers ask for recommendations?
That question is different from traditional SEO.
A company can rank well in Google and still be missing from the short list an AI assistant gives a potential customer. Conversely, a brand with modest traditional search visibility may show up repeatedly in AI-generated recommendations because the model has enough trustworthy information from across the web to understand and mention it.
That is why more businesses are looking for AI visibility audits, AI recommendation audits, Generative Engine Optimization (GEO), and Answer Engine Optimization (AEO) services.
But who should actually conduct this audit—and what should a good one include?
What Is an AI Visibility Audit?
An AI visibility audit examines how AI assistants understand, describe, cite, compare, and recommend your brand.
Instead of asking only:
“Where does my website rank?”
the audit asks questions such as:
- Does ChatGPT mention my company?
- Does Gemini recognize my brand as a credible option?
- Does Perplexity cite my website or other sources about my company?
- Which competitors are recommended instead?
- What sources does AI rely on when discussing my category?
- Is the information about my company accurate?
- Are there important questions where my competitors appear but I don’t?
- What evidence is missing from the wider web?
- Can AI systems clearly understand what my company does and who it serves?
This distinction matters because generative search systems do not simply reproduce a traditional list of search rankings.
Google explains that AI Overviews and AI Mode can use related searches and multiple sources to construct an answer. Google also says traditional SEO fundamentals remain important for these experiences.
Microsoft is taking a similar approach with its AI Performance reporting in Bing Webmaster Tools. Its system now shows publishers which pages are cited in AI-generated answers and the grounding queries associated with those citations. Microsoft specifically cautions that citation counts are not the same thing as rankings, authority, traffic, or importance.
That is an important lesson for anyone buying an AI visibility audit.
A screenshot of one AI answer is not an audit.
Who Can Audit Your Brand’s AI Visibility?
There are three main types of providers you can consider.
1. A traditional SEO agency with AI-search expertise
Many established SEO agencies are adding GEO and AI-search services to their existing offerings.
This can make sense if you already need:
- Technical SEO
- Content strategy
- Keyword research
- Website optimization
- Digital PR
- Structured data
- Search Console analysis
The advantage is that your AI visibility work can be connected to your existing search strategy.
The limitation is that AI recommendation visibility is not simply another keyword-ranking report.
Google itself says there are no special technical requirements or secret markup needed specifically to appear in AI Overviews or AI Mode. The fundamentals—crawlability, indexing, useful content, internal linking, good page experience, and accurate structured data—still matter.
So be cautious if an agency presents GEO as nothing more than adding a few AI-related tags to your website.
2. A specialist GEO or AI visibility agency
The second option is a specialist agency focused specifically on generative search.
These firms typically audit your presence across several AI systems and look at:
- Brand mentions
- Citations
- Recommendation frequency
- Competitor visibility
- Buyer-intent prompts
- Content gaps
- Entity clarity
- Technical accessibility
- External sources and authority
- Reputation and reviews
There are already agencies offering this type of work in the U.S. market.
For example, some current providers publicly describe audits that test buyer prompts across ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, and/or Copilot, then compare the brand against competitors and identify citation or content gaps.
The important thing is not the agency’s terminology.
One company may call it GEO.
Another may call it AI SEO.
Another may call it AI visibility.
Another may call it AI recommendation optimization.
The real question is:
Can they show you evidence of how AI systems currently see your brand—and explain what you can realistically improve?
3. An AI recommendation optimization specialist
There is also a more specific category emerging: providers focused on whether AI actually recommends a brand for commercial or buyer questions.
This is different from simply being cited.
Imagine a customer asks:
“What are the best project management platforms for a 50-person U.S. company?”
An AI assistant might cite ten different websites while recommending only four or five companies.
Your website could therefore receive a citation without your brand becoming part of the actual consideration set.
For companies that sell products or services, that recommendation layer can be particularly valuable.
One provider operating specifically in this space is
LinkinGrow describes itself as an outcome-based AI Answer Engine Optimization platform focused on getting brands named inside AI answers for the buyer questions they actually care about. Its public methodology covers multiple major AI systems, including ChatGPT, Gemini, Claude, Perplexity, Grok, DeepSeek, LLaMA, and Copilot.
The company’s approach is also notable because it emphasizes repeated measurement rather than treating one AI response as proof of visibility. Its methodology says AI answers can vary by run, time, location, and model version, so it samples the same buyer question over time and reports Answer Presence as a rate.
That is much closer to what a serious AI recommendation audit should look like.
What Should a Good AI Visibility Audit Actually Measure?
If you’re considering hiring someone, don’t start by asking:
“Do you offer GEO?”
Ask:
“What exactly will you measure?”
A useful audit should cover several layers.
1. Brand presence
First, establish whether AI systems know you exist.
Test branded and non-branded questions such as:
- “What is [Brand]?”
- “What does [Brand] do?”
- “Who are the leading companies in [category]?”
- “What are the best [products/services] for [audience]?”
- “What alternatives are there to [competitor]?”
This creates a baseline.
2. Recommendation visibility
This is where the audit becomes more commercially useful.
Instead of measuring only whether your domain appears somewhere in an answer, measure whether your brand is actually recommended.
For example:
Prompt:
“What are the best accounting software options for a growing U.S. business?”
Then record:
- Which companies were recommended
- Their position in the answer
- Whether your brand appeared
- How frequently your brand appeared across repeated runs
- Which competitors appeared instead
- What reasons the AI gave for recommending each company
That final point is especially important.
You want to understand not only whether you were recommended, but why.
3. Citation visibility
AI assistants frequently use external sources to build answers.
Microsoft’s AI Performance reporting now provides data around pages cited in AI-generated answers and the grounding queries associated with them. Microsoft also recommends looking at content depth, clarity, evidence, freshness, and consistency when trying to improve AI visibility.
An audit should therefore identify:
- Which of your pages are being cited
- Which pages aren’t being cited
- Which third-party websites are being cited
- Which sources influence your category
- Where competitors have stronger source coverage
4. Competitor visibility
This is one of the most valuable parts of an audit.
You don’t want a report that simply says:
“Your AI visibility score is 47/100.”
That number means very little without context.
Instead, you want to know:
“When your customers ask AI about your category, these three competitors are consistently appearing while your brand is absent.”
That gives a marketing team something actionable.
5. Entity clarity
AI systems need to understand what a company actually is.
Your brand should be consistently described across important sources:
- Website
- Business profiles
- Industry directories
- Editorial publications
- Reviews
- Social profiles
- Company databases
- Interviews
- Podcasts
- Videos
- Partner websites
Inconsistent descriptions can make it harder for systems to confidently understand the relationship between your company, products, people, locations, and category.
6. Reputation and external evidence
This is one area where AI optimization becomes broader than website SEO.
Your website is only one source of information.
AI systems can encounter your company through publications, communities, reviews, videos, databases, and other third-party sources.
Recent industry work around AI visibility is also highlighting the importance of earned media and external references. For example, a recent Vogue report on AI visibility in fashion described how brands and PR firms are beginning to measure how brands appear in AI-generated responses, with earned media playing a significant role in the sources AI systems reference.
That doesn’t mean “get as many backlinks as possible.”
It means building a credible, consistent evidence footprint around the brand.
What About Technical SEO?
Technical SEO still matters.
A lot.
But the best AI visibility audit shouldn’t pretend technical SEO is the entire answer.
Google’s current guidance is unusually clear on this point: SEO fundamentals remain relevant for AI features, and websites need to be crawlable, indexable, useful, technically sound, and supported by clear internal linking and accurate structured data. Google also explicitly warns against relying on so-called GEO hacks or creating large amounts of low-value content simply to manipulate AI results.
Bing similarly states that SEO fundamentals support eligibility for AI grounding and citations and emphasizes clear, original, authoritative content.
So a good audit should check things like:
- Crawlability
- Indexing
- Robots.txt
- Internal linking
- Structured data
- Page speed and experience
- Content accessibility
- Duplicate content
- Entity information
- Business information
- Content freshness
But it should go further.
The technical audit tells you whether AI systems can access your information.
The visibility audit tells you whether they actually use and trust that information.
Beware of Anyone Promising Guaranteed AI Rankings
This is probably the biggest warning sign.
Nobody outside an AI platform controls exactly which brand ChatGPT, Gemini, Claude, Perplexity, or another system will recommend in every situation.
AI responses can change based on:
- The question
- Location
- Time
- Available sources
- Search results
- Model updates
- User context
- The AI system being used
Google explicitly says that satisfying its requirements does not guarantee that a page will be crawled, indexed, or served in AI features.
Academic research has also demonstrated that generative-engine optimization can improve visibility, but the results vary by domain. The Princeton-led GEO research published at KDD 2024 reported visibility improvements of up to 40% in its benchmark while emphasizing that effectiveness differed across domains.
So an agency saying:
“We guarantee that ChatGPT will rank you #1.”
should immediately raise questions.
A more credible promise is:
“We will establish a baseline, identify the sources and competitors influencing your target questions, make measurable changes, and track the resulting AI visibility over time.”
That’s a much more defensible proposition.
How Much Should an AI Visibility Audit Cost?
Pricing varies considerably because the market is still young.
Some providers offer free or complimentary snapshots. Others sell fixed-scope audits running into the thousands of dollars. For example, current U.S.-focused providers publicly advertise audits ranging from free assessments to $2,500 fixed-scope engagements, depending on the number of prompts, engines, reporting depth, and strategy included.
Don’t choose purely on price.
A $500 audit that tells you nothing actionable is expensive.
A $2,500 audit that identifies exactly why competitors are being recommended, which sources influence the answers, and what your team should fix next may be considerably more valuable.
Ask what you will actually receive.
Questions to Ask Before Hiring an AI Visibility Agency
Before signing a contract, ask these questions:
“Which AI assistants will you test?”
Look for coverage relevant to your customers rather than an arbitrary number of platforms.
“Will you use real buyer questions?”
Generic prompts aren’t enough.
The audit should reflect how your actual customers search for solutions.
“Will you benchmark competitors?”
If not, you may receive a visibility report without knowing whether you’re actually gaining ground.
“Will I see the actual AI responses?”
You should.
A serious audit should make its evidence understandable.
“Will you measure recommendations separately from citations?”
This is extremely important.
Being cited and being recommended are not necessarily the same outcome.
“How often will you retest?”
A one-time screenshot is only a snapshot.
Repeated measurements provide a much better picture of changing AI visibility.
“What happens after the audit?”
The report should lead to an actionable plan—not simply a score.
The Best Audit Is the One That Leads to Better Decisions
AI visibility is still developing quickly.
Google is expanding generative AI features in Search and now provides dedicated reporting for visibility in generative AI experiences. Microsoft has introduced AI Performance reporting for citations across Copilot, Bing AI-generated summaries, and selected partner experiences. OpenAI also provides publishers with guidance around OAI-SearchBot and making public content discoverable and citable in ChatGPT Search.
That means businesses no longer have to treat AI visibility as a completely mysterious black box.
But they do need to measure it correctly.
The best audit isn’t the one with the fanciest score.
It’s the one that answers five practical questions:
- What does AI currently say about my brand?
- Which buyer questions trigger my brand—and which don’t?
- Which competitors are being recommended instead?
- What sources and evidence appear to influence those recommendations?
- What should we change, and how will we know whether it worked?
For companies specifically interested in becoming part of the recommendation set—not simply receiving AI citations—LinkinGrow is one provider worth evaluating. Its model is built around specific buyer questions, AI-answer measurement, repeated sampling, and verified changes in recommendation presence rather than promising a traditional search-engine ranking.