A typical SEO audit checks what matters for Google: page speed, Core Web Vitals, meta tags, broken links, duplicate content. All of that still matters. But when ChatGPT, Claude, or Gemini generate a response for a user, they look at completely different things.
Language models don’t index your site like a search engine. They retrieve content by semantic patterns, not keyword density. They extract structured claims, not keyword phrases. They cite passages that are easy to parse, not the ones that rank best in SERPs. And they completely ignore content blocked for their specific crawlers.
The result: a site can be perfectly optimized for Google and completely invisible to AI.
These are two different optimization problems. And until now, there wasn’t a specialized tool for the second one.
What does AI SEO Checker look for?
AI SEO Checker is an autonomous audit agent that crawls your site and answers one question: what’s preventing language models from properly understanding, extracting, and citing your content?
The audit covers dozens of parameters across several key categories.
AI crawler accessibility
Every major model has its own crawler: ChatGPT-User, ClaudeBot, PerplexityBot, GPTBot, Google-Extended, Bytespider. Many sites block some of them through robots.txt, sometimes without even realizing it.
AI SEO Checker verifies which of these crawlers can access your site and which are blocked. It also checks for the presence of an llms.txt file, an emerging standard that helps language models quickly understand your site’s structure and content.
Structured data for AI extraction
Schema.org markup (Organization, Product, FAQPage, HowTo, Article) is one of the primary ways AI models “understand” what’s on a page. The checker verifies the completeness of your JSON-LD markup and the presence of schema types relevant to your category.
Example: if you’re a SaaS product without a Product schema, the model receives raw text during retrieval and has to figure out what you offer on its own. Structured data helps the model understand your product faster and describe it more accurately.
Content extractability
This is where traditional SEO audits don’t look at all: how easily can a language model pull standalone claims from your content?
Semantic HTML structure, content-to-code ratio, passage clarity (can the text yield self-contained statements?), and factual density (does the text contain specific numbers, dates, and facts that a model can cite?).
Entity clarity
Is your brand clearly identified on the page? If your site says “we” and “our product” everywhere instead of using your company name, it’s harder for models to build entity associations. The checker evaluates how consistently and unambiguously your brand is represented across the site and linked to your category.
FAQ and Q&A structure
This one is often underestimated. Well-structured FAQs with FAQPage schema are one of the most reliable ways to get into AI responses. The checker looks at whether your site has explicit Q&A sections, whether they’re marked up with the right schema, whether questions are phrased as real user queries, and whether answers provide standalone factual claims.
Citation-ready blocks
Language models cite what’s easy to cite. Numbered lists, comparison tables, data with attribution, “quotable” passages of 1-2 sentences, TL;DRs or summaries at the top of long-form content. If none of this exists on your site, the model has to process your content from scratch, and more often than not, it will cite a competitor who has the answer ready to go.
Freshness signals
Visible publication and update dates. Version history for product pages. Regular new content activity. For models that factor in data freshness during retrieval (especially Perplexity), this is a critical signal.
Technical AI-readability issues
JavaScript-rendered content that LLM crawlers often can’t execute. Infinite scroll and lazy-loaded blocks invisible to bots. Iframe content that gets skipped during extraction. Images without alt text that simply don’t exist for a language model.
How it works
The process is simple: enter your site URL or a specific page, the agent crawls it like a real AI crawler, and generates a structured report.
The report includes an Overall AI Readiness Score from 0 to 100 with a grade (A through F), a breakdown by each audit category with its own score, a Critical Findings section highlighting the highest-impact issues, a Strengths table showing what’s already working, and a Detailed Breakdown where every check shows its status (Pass, Partial, Fail) with an explanation.
On the Pro plan, you also get ready-made code solutions for each issue. Not a vague “add schema,” but a specific snippet ready to paste. For example, if AI Search Bot Access is marked as Fail, the Pro report shows the exact lines for your robots.txt with the correct User-agent directives for each AI crawler.
Why a traditional SEO audit won’t help here
Traditional site audit tools do their job well, for Google. They find broken links, slow pages, duplicate content, meta tag issues. That’s a different task.
They don’t know about AI-specific crawlers or llms.txt. They don’t check entity clarity or passage quotability. They don’t assess how well your content lends itself to extraction and citation by language models. They don’t provide recommendations in the context of your category with AI visibility metrics in mind.
This isn’t a criticism. These are different tools for different jobs. But if you’ve run a traditional audit and decided your site is “fine,” you’ve only checked half the picture.
| What’s checked | Traditional SEO audit | AI SEO Checker |
|---|---|---|
| Page speed / Core Web Vitals | Yes | No |
| Broken links | Yes | No |
| Meta tags / titles | Yes | Partial |
| Schema.org (AI-specific) | Partial | Yes |
| AI crawler access (llms.txt) | No | Yes |
| Robots.txt for AI bots | No | Yes |
| Entity clarity | No | Yes |
| Passage quotability | No | Yes |
| FAQ structure for AI | No | Yes |
| Citation-ready blocks | No | Yes |
| Content extractability | No | Yes |
| Freshness signals (AI-weighted) | Basic | Yes |
Why we built this
Rankry was designed from day one as a full AI visibility cycle: not just showing where you stand, but giving you the tools to improve it. The Core Report shows your positions, deal breakers, and competitor comparisons. AI SEO Checker covers the next step: making sure your site is technically ready for AI models to read and cite it properly.
Traditional SEO tools don’t answer AI-specific questions. They don’t know about llms.txt, don’t check AI crawler accessibility, don’t evaluate content extractability. AI SEO Checker was built specifically for this.
What’s available
Base plan: 4 audits per month with a full report, scores for each category, and a prioritized list of issues.
Pro plan (coming soon): up to 15 audits per month, ready-made code solutions with copy-paste snippets for every issue, integration with the main Core Report.
The audit takes seconds. The result is a complete picture of what’s preventing AI models from working with your site.
FAQ
How is AI SEO Checker different from a regular SEO audit? A traditional SEO audit checks your site through Google’s lens: speed, meta tags, backlinks. AI SEO Checker looks through the lens of language models: can ChatGPT, Claude, or Gemini properly extract, understand, and cite your content? These are different optimization systems, and one doesn’t replace the other.
Do I need an llms.txt file on my site?
It’s an emerging standard, but adoption is growing fast. llms.txt helps AI crawlers quickly understand your site structure and prioritize key pages. If you’re serious about AI visibility, it’s worth adding now. AI SEO Checker will show you how to set it up correctly.
How often should I run an AI audit? After significant site changes (redesign, new pages, content updates) and once a month for ongoing monitoring. AI crawlers update their indexes more frequently than Google, so changes to your site can show up in AI responses faster, sometimes within days.
