Run a structured AI visibility audit across technical access, entity clarity, content coverage, evidence, and point-in-time AI search presence. See the findings behind the diagnosis instead of relying on one black-box score.
Growthract
AI visibility audit stack
Access
Can important information be retrieved?
Entity
Is the company and product clear?
Content
Are buyer questions answered?
Evidence
Are important claims supported?
Visibility
Does the brand appear in tested prompts?
Point-in-time diagnosis first. Ongoing visibility tracking comes after the baseline is established.
Want a benchmark before auditing your own site? Our original research measures many of the same observable technical signals across 100 B2B SaaS websites.
Definition
An AI visibility audit is a structured review of the conditions that may affect whether a business can be retrieved, understood, mentioned, or cited in AI-assisted search.
It combines established SEO foundations such as crawlability, rendering, site structure, metadata, and useful content with AEO and AI SEO questions around entity clarity, answer coverage, supporting evidence, prompt-level visibility, and competitor presence.
The purpose is diagnosis. The audit should tell you what can be observed, where the gap exists, why it deserves attention, and what action is worth considering next.
Want something lighter first? See what a free AI search visibility checker tests before committing to the full audit below.
What the audit checks
A missing mention does not automatically tell you what is wrong. The audit works backward through the layers that can actually be investigated.
Check whether important pages and information are accessible through the routes, crawler rules, responses, metadata, canonicals, and initial HTML that retrieval systems can inspect.
Example checks
robots.txt and crawler access
priority route availability
HTTP response behavior
initial HTML and rendering dependencies
canonical and metadata consistency
Inspect whether your company, product, audience, category, pricing, features, and relationships are represented consistently enough to understand what the business actually is.
Example checks
company and product identity
category positioning
audience and use-case clarity
structured entity relationships
cross-page consistency
Review whether commercially important questions have clear, useful answers across product, category, comparison, use-case, pricing, implementation, and educational pages.
Example checks
direct answers to buyer questions
comparison and alternative coverage
use-case and audience coverage
commercial information clarity
supporting internal links
Identify where important claims, expertise, product information, and category associations are supported by useful first-party or independent evidence.
Example checks
source attribution
original research and evidence
third-party references
community and industry mentions
consistency beyond your own domain
Use repeatable point-in-time prompt testing to observe whether your brand appears, which competitors appear instead, how the brand is described, and whether sources are surfaced.
Example checks
brand mentions
competitor mentions
prompt coverage
citation or source presence
answer context
Audit vs tracking
An AI visibility audit and an AI visibility tracker solve different problems. The audit establishes the baseline. Tracking measures what changes after the baseline exists.
AI visibility report
The output should help you decide what deserves attention. It should not hide the diagnosis behind an unexplained score.
What was actually found in the website, structured data, response output, or tested AI-search result.
Which commercially important URLs or information layers are connected to each finding.
The retrieval, entity, content, evidence, or visibility consequence associated with the issue.
Which findings deserve attention first based on evidence and likely business relevance.
The concrete next step rather than a vague instruction to simply create more content.
What the audit can observe and what cannot legitimately be inferred from a third-party AI system.
AI SEO audit + AEO audit
AI SEO and AEO do not replace technical SEO. If important pages are difficult to retrieve, poorly rendered, inconsistently canonicalized, weakly connected, or unclear about what the business actually offers, AI-specific tactics cannot erase those fundamentals.
The audit therefore starts with observable search and website conditions before expanding into entity understanding, answer-ready content, supporting evidence, and AI-search visibility.
DIY or automated?
Manual review is valuable because it forces you to think about positioning, product clarity, content usefulness, evidence, and buyer questions directly.
Automation becomes useful when the work expands across more URLs, repeated checks, structured findings, technical patterns, and before-and-after comparisons.
Follow the DIY AEO auditFrequently asked questions
An AI visibility audit is a structured review of the conditions that may affect how easily a business can be retrieved, understood, mentioned, or cited in AI-assisted search. It can include technical accessibility, entity clarity, content coverage, supporting evidence, and point-in-time AI-search observations.
The terms overlap. An AEO audit typically evaluates whether a website is prepared for answer-driven search through technical accessibility, entities, answer-ready content, evidence, and visibility. An AI visibility audit emphasizes the observable visibility outcome as well as the underlying AEO and AI SEO conditions that may contribute to it.
An AI SEO audit extends traditional SEO analysis into AI-assisted discovery. Alongside crawlability, rendering, metadata, content, and site structure, it can examine entities, machine-readable context, buyer-question coverage, AI-search mentions, competitor visibility, citations, and external evidence.
Growthract examines observable technical and website signals such as crawler access, initial HTML, metadata, canonicals, structured data, entity clarity, content coverage, and point-in-time AI visibility observations. Findings are separated from assumptions about proprietary third-party ranking systems.
No. An audit is primarily a diagnostic used to establish what is happening and why. An AI visibility tracker repeatedly measures defined signals such as brand mentions, competitor presence, prompt coverage, citations, and changes over time.
A useful AI visibility report should document the methodology, pages and prompts examined, observable findings, brand and competitor presence, citation or source visibility where available, technical and content gaps, recommended actions, priorities, and important limitations.
No. An audit cannot guarantee that ChatGPT, Google, Perplexity, Gemini, Claude, or another third-party system will rank, recommend, mention, or cite a company. It can identify observable conditions and gaps that deserve investigation or improvement.
Yes. A manual audit is useful for understanding a smaller set of important pages. Automation becomes more useful when you need consistent checks across more URLs, structured evidence collection, repeatable comparisons, and prioritization.