An AI visibility audit should produce a benchmark, not a vague overview. As customer discovery shifts into platforms like ChatGPT, Gemini and Perplexity, a brand ranking well on Google can still be nearly invisible in AI-generated answers.
Brands need a repeatable way to measure whether they appear in AI-generated answers, how competitors compare, which pages get cited and where messaging breaks down.
That’s where an AI search audit — sometimes called an AEO audit — comes in. Unlike a traditional SEO audit, which focuses on rankings and keywords, an AI visibility audit measures brand mentions, AI citations, message accuracy and share of voice across multiple AI platforms.
We spoke with Jason Patel, CEO and co-founder of Open Forge AI, to unpack how these audits work and what they reveal.
What an AI visibility audit measures
“An SEO audit is about rankings, and an AEO audit is about seeing where you show up,” explains Patel. “With AEO, you're looking at where your brand is chosen. You basically want to boost the chances of your brand being cited when AI chooses to surface you.”
A strong AI visibility audit tracks mentions, citations, sentiment and share of voice across AI systems, including:
- Whether your brand appears in AI-generated responses
- Whether those responses include citations back to your site
- How accurately your company is described
- Which competitors consistently appear beside you.
Patel says to pay attention to both mentions and citations, but notes that citations matter more because they drive people back to your website. He also recommends tracking the kinds of responses these platforms generate about your brand.
“If you are mentioned or cited, you want to look at answer integrity: how is the brand talked about by AI? Is it correct?” Patel says.
Why AI visibility and SEO visibility diverge
AI search visibility changes faster than traditional SEO because AI models pull information from multiple sources and continuously reinterpret categories, competitors and trust signals.
With AEO, Patel says, “You generally want to focus on commercial-level intent queries, and once you find them, map out where you show up.”
See also: AEO for partnerships: How to rank in answer engines.
The 30-minute audit framework
This framework breaks AI search visibility into a simple, repeatable process you can use to benchmark performance over time.
0–5 minutes: define your prompt set and competitors
Patel says the quality of an AI visibility audit depends on the quality of the prompts being tested.
“You want to go into Zendesk or your tickets, your customer emails, and literally take the voice of the customer and then reverse engineer that to create prompts,” Patel says. “It’s the most important thing. If you have the wrong prompts, you'll have the wrong audit.”
Rather than testing broad prompts like “best CRM software,” Patel recommends using queries tied to real buying intent and customer needs.
“You want to show up with a question that's much more specific with constraints,” Patel says. “So, what are the best email marketing tools for companies under $1 million in ARR marketing toward the governance and compliance in B2B SaaS spaces?”
To build your own high-intent prompts, you can follow a similar formula:
What are the best [category] tools for companies under [$XX ARR] in [industry/use case]?
The same prompt set should be reused consistently to create a repeatable AI search benchmark.
5–15 minutes: test ChatGPT, Gemini and other platforms
Once the prompts are established, brands should test the same queries across the major AI platforms people use to discover products.
Patel recommends prioritizing ChatGPT, Gemini and Claude.
“ChatGPT has brand power,” Patel says. “Gemini has the power of Google, which has a built-in infrastructure. Claude has been wonderful for enterprise applications.”
During this stage of the AI search audit, document each result using a simple consistency check. For example, you can copy these five metrics into a spreadsheet or audit worksheet to benchmark each prompt across platforms:
- Brand mention — does the brand appear? (yes / no)
- Citation — do responses include AI citations? (yes / no)
- Accuracy of description (correct / partial / incorrect)
- Competitors — are competitors shown alongside you? (yes / no)
- Pages referenced — which pages do AI systems reference?
A single audit entry might then look like this:
- Prompt: “Best partner tracking tools for companies under $10 million ARR in B2B SaaS”
- Brand mentioned: Yes
- Cited page: /partner-program-software
- Message accuracy: Partial
15–20 minutes: log cited pages, messaging accuracy and missing prompts
After collecting responses, the next step is identifying patterns in citations, descriptions and competitor visibility.
Patel says this stage should focus on whether the answers are accurate and whether new customer questions are emerging. Treat missing prompts as something to keep refining instead of a one-time checklist item.
“Your AEO audit will never end,” Patel says. “The more AEO topics you hear about from your customers, the better off you're going to be.”
20–25 minutes: review your highest-priority owned pages
Once you identify which pages AI systems are citing most often, review them closely. The goal is to understand how AI platforms interpret and evaluate your brand.
“You're trying to reverse engineer how AI learns about your brand,” Patel says. “What are the trust surface areas of your brand that are being linked to on those AI search engines?”
Brands should focus on pages that already drive conversions because AI search changes how people discover and act on information online.
“If you're optimizing your page correctly, those big victory pages that you have, the better off you're going to be,” Patel says.
For that reason, optimization is no longer just about SEO.
“Optimizing your pages is not just an SEO practice anymore,” Patel says. “It's a new AEO practice too.”
See also: How AEO is like early SEO — and why first principles will win.
25–30 minutes: identify the gaps and set the next actions
The final step is deciding what to do next. Once the patterns are clear, teams need to prioritize what to fix.
Common content gaps uncovered in a fast audit include:
- Missing comparison content: Many companies still lack comparison content that reflects how customers actually research products.
- Inconsistent positioning and terminology: Companies often describe themselves differently across product pages, blogs, review sites and external profiles, which can confuse AI systems.
- No third-party validation around priority claims: AI systems rely heavily on external trust signals for expensive or high-risk purchases. Because of this, it’s important to track which review sites and third-party publishers consistently appear in AI-generated responses.
These gaps in the content ecosystem directly affect how AI systems form answers about your brand. Identifying them can then translate into actions such as:
- Gap: missing or unclear messaging
- Potential fix: updating positioning copy or messaging
- Gap: weak or missing structured data
- Potential fix: improving structured data or page clarity
- Gap: lack of comparison content
- Potential fix: expanding comparison or category content
“If you don't like how AI is talking about you… change the copy on those pages,” Patel says.
See also: How partner leaders can help marketing win AI visibility and prove AEO ROI.
What to look for in the responses
Focus on what the responses reveal about your brand’s visibility and positioning.
Are you mentioned?
Mentions show whether AI search tools associate your company with a category or use case at all.
Are you cited?
AI citations indicate whether systems are pulling from your content and linking back to your site — and often matter more than mentions.
Are you described correctly?
Check whether AI systems are categorizing your company accurately and reflecting the right positioning.
Which competitors appear by default?
Track which brands consistently appear alongside yours in AI-generated responses, as these associations can influence future visibility.
The technical checks that belong in a fast audit
Technical checks don’t replace content analysis, but they can help explain why visibility gaps appear.
Crawl access and AI bot allowances
If AI crawlers cannot access important pages, your visibility will suffer.
Structured data validation and page clarity
Patel says structured data and schema still matter because they help AI systems understand pages correctly.
Machine-readable product and category pages
AI systems need clear signals about what each page represents.
“Remove friction from the crawler getting to know your website,” Patel says. “If you're able to properly classify what your pages are to the AI, it's going to have more of an incentive to cite you.”
Turning a benchmark into an operating system
The goal isn’t just to run an AI visibility audit once — it should become an ongoing measurement system.
Which metrics to track monthly
When it comes to tracking key performance metrics for your AI visibility, Patel recommends monitoring mentions, citations, competitor visibility and which pages AI tools cite most often.
“Mentions and citations are the two big ones,” Patel says.
See also: Introducing PartnerStack’s Content Marketplace: Activate AEO data and drive AI visibility at scale.
When to escalate from a 30-minute audit to a deeper AEO program
Patel says brands should escalate from quick audits into a more structured AEO strategy once AI search starts driving customers and revenue.
In practice, that shift shows up through signals like consistent citations across key prompts, early inbound leads attributed to AI search or competitors beginning to dominate category definitions in responses.
“Whenever you start getting customers through AEO is when you need to start ramping up your program,” Patel says.
He recommends increasing audit frequency as AI search starts bringing in more meaningful leads and customers.
“If you're an online business, this is the surface area of where all of it is going,” Patel says. “I think you should do it once every six weeks.”
As AI search continues evolving, brands that benchmark visibility consistently will be better positioned to understand — and influence — how they appear in AI-generated discovery.
