Technical SEO discoverability
Checks crawlability, indexability, canonicals, sitemaps, robots directives, and other technical signals that affect whether AI systems can reach the right pages.
What we do
LLM visibility audit for SEO, AEO, GEO and AI search
Crawlly AI gives you a dedicated LLM visibility audit that shows whether AI systems can find your site, understand your brand, cite your pages, and represent your offer accurately. We connect technical SEO, structured data, AEO, GEO, retrieval readiness, and prompt-level evidence in one report so teams can move from guesswork to execution.
Inside Crawlly AI
A report built to show what AI systems see and what your team should fix next
Mention rate, citation rate, citation rank, category findings, role-based actions, and a single roadmap all live in one audit flow, so you are not left translating raw metrics into decisions.
Mention rate
Citation rate
Citation rank
Category findings
Role actions
Single roadmap
Useful for
SEO teams
Map crawlability, entities, citations, and technical blockers into one fix list.
Content teams
See where pages need stronger answers, evidence, and citation-ready structure.
Growth leads
Understand how AI visibility affects demand capture, discoverability, and trust.
Agencies
Show clients a clearer audit narrative with prompt evidence and an execution roadmap.
Questions this audit answers
What a LLM visibility audit should answer
This belongs lower on the page because it supports the offer instead of interrupting the first read. A strong audit should answer the operational questions your team actually needs before making changes.
Can AI systems reach the right pages?
Do they understand your brand and entities correctly?
Are your answers and evidence easy to extract?
Does your site earn mentions and citations in real prompts?
What should engineering, SEO, and content fix first?
What we do
What a LLM visibility audit checks
A useful LLM visibility audit needs more than prompt testing. It should diagnose the full stack behind AI discoverability: access, understanding, answer structure, citation readiness, and the evidence layer that influences whether your brand is included in real AI outputs.
Entity and schema clarity
Verifies structured data, entity consistency, and brand context so LLMs can connect your company, products, services, and claims without confusion.
AEO answer formatting
Measures whether your pages are easy to turn into direct answers through Q&A structure, FAQ coverage, concise summaries, and answer-first formatting.
GEO citation readiness
Looks for source-backed claims, statistics, quotable blocks, and evidence density that improve your odds of being cited in generative search experiences.
Prompt-level LLM visibility
Runs prompt suites to measure brand mentions, citation rate, citation rank, and answer quality across the commercial and informational queries that matter.
Execution-ready roadmap
Turns findings into prioritized issues, role-based actions, and example fix paths so SEO, engineering, and content teams can ship improvements faster.
Why it helps
Why teams use a LLM visibility audit
This is a separate layer from what the audit checks. It explains why the workflow is useful once the findings start turning into decisions for SEO, content, and technical teams.
1
See why AI systems miss or misstate your site
A real LLM visibility audit should not stop at rankings or crawl stats. It should explain where discoverability breaks, where understanding breaks, and where citations fail.
2
Connect SEO, AEO, GEO and LLM evidence in one workflow
This matters because LLM visibility problems are rarely caused by one layer alone. Technical SEO, structured data, answer formatting, and evidence quality all compound together.
3
Give every team a clearer next step
Instead of one vague score, teams get the context to act: what is wrong, why it matters, who owns it, and what a good fix should look like.
Outputs
What you get from Crawlly AI
Executive score, risk band, and plain-language summary of what is limiting AI discoverability.
Category views for technical SEO, structured data, retrieval readiness, internal authority, competitive intent, GEO, and AEO.
Prompt-level LLM evidence showing mention rate, citation rate, citation rank, captured answers, and supporting sources.
A single roadmap with priority, owner, focus area, status, and example fix guidance for each issue.
Role-based actions for technical, copywriter, and owner workflows so execution is easier to coordinate.
Repeatable audit runs so you can compare what improved, what regressed, and what still needs attention.
Framework
How SEO, AEO, GEO and LLM visibility fit together
LLM visibility is not a separate channel that replaces SEO. It sits on top of the same infrastructure and content quality signals, then adds answer formatting, citation readiness, and prompt-level validation.
SEO is the access layer
If important pages are hard to crawl, canonicalized incorrectly, or poorly linked, LLM systems start with weak input.
AEO is the answer layer
If answers are buried in long copy or missing FAQ structure, AI systems have less clean material to quote or summarize.
GEO is the citation layer
If pages lack clear evidence, statistics, and source-backed claims, you lower the chance of being cited in generative outputs.
LLM visibility is the outcome layer
This is where you verify whether the combined work actually changes mentions, citations, and answer quality for your brand.
FAQ
Common questions about LLM visibility audits
Question
What is an LLM visibility audit?
Answer
An LLM visibility audit checks whether AI systems can discover, understand, mention, and cite your brand accurately. It combines technical SEO, structured data, answer formatting, GEO signals, and prompt-based validation in one workflow.
Question
How is an LLM visibility audit different from a standard SEO audit?
Answer
A standard SEO audit focuses on crawling, indexing, rankings, and site health. An LLM visibility audit adds the layers that matter for AI search: entity clarity, citation readiness, answer formatting, prompt outcomes, and whether the model actually references your site.
Question
Why do AEO and GEO matter in a LLM visibility audit?
Answer
AEO helps answer engines and chat systems extract concise, direct responses. GEO helps generative systems find source-backed content worth citing. Without both, you may have indexable pages that still underperform in AI-generated answers.
Question
What does Crawlly AI measure in the audit?
Answer
Crawlly AI measures technical discoverability, schema and entity signals, retrieval and chunk quality, internal authority, competitive intent coverage, GEO and AEO readiness, and prompt-level mention and citation outcomes.
Question
Who should use a LLM visibility audit?
Answer
This is useful for SEO teams, content teams, growth leaders, product marketers, and agencies that need a clearer view of how their site performs in AI search and answer experiences.
Question
What happens after the audit?
Answer
You get a prioritized roadmap with role-based actions and example fix guidance. Then you can implement the changes, rerun the audit, and compare whether mentions, citations, and supporting visibility signals improve.
Ready to measure it?
Run a LLM visibility audit and turn the findings into an execution plan
Crawlly AI gives you one report for technical SEO, AEO, GEO and prompt-level LLM evidence so your team can improve discoverability, mentions, and citations with clearer next steps.