Your domain rating looks good. Pages are indexed. Rankings are solid.
But here’s the question your analytics tool can’t answer: when someone asks ChatGPT or Perplexity about your industry, does your brand come up?
For most websites, that’s completely unknown – and it’s a gap that’s growing fast.
Search engines rank. AI systems interpret.
When a crawler visits your page, it’s measuring signals: backlinks, load speed, keyword match. The output is a ranked list.
When a language model processes your content, it’s doing something different. It’s asking: Can I extract a clear answer from this? Is it specific enough to trust?
The criteria are different:
Semantic clarity. Can the meaning of a section be understood without the rest of the article?
Answer modularity. Can individual paragraphs be lifted and reused as standalone responses?
Entity precision. Are names, tools, and concepts explicitly defined – or just implied?
Structured signals. Schema markup and clear heading hierarchies help AI systems assign meaning, not just find keywords.
None of this shows up in a standard SEO audit. Which is exactly the problem.
This is what LLMO is about
The discipline that addresses this gap is called LLMO – Large Language Model Optimization.
It sits alongside traditional SEO, not against it. The fundamentals still matter: authoritative content, clear structure, topical depth. But LLMO adds a deliberate layer focused on machine interpretability – making your content not just discoverable, but usable when an AI generates a response.
In practice, that means writing so that any section could stand alone as an answer. Using schema not just for rich snippets, but to tell AI systems what your content is – not just what it’s about. And thinking less about keyword density and more about how a paragraph reads when it’s extracted out of context.
Platforms like Geordy.ai were built specifically for this: automatically generating the structured formats – JSON-LD, YAML, Markdown, llms.txt – that AI systems need to understand and cite your content reliably.
The llms.txt problem nobody is checking
One of the most practical steps toward AI visibility is implementing a llms.txt file – similar to robots.txt, but designed for AI crawlers like GPTBot and ClaudeBot. It tells them what they can access, how to attribute it, and what context to preserve.
The problem: most teams create the file, upload it, and never verify it.
llms.txt is a new and evolving standard. A syntax error or a wrong directive can silently fail – either giving crawlers no useful instructions, or accidentally blocking content you want surfaced.
Here’s one common issue a real validation run turns up – and it’s easier to miss than you’d think:
The issue:
# Example Company
# Website Overview
# Key Features
# Company Information
Four # headings in a single file. The spec allows exactly one. AI crawlers that encounter this either misread the file’s structure entirely or skip it.
The fix:
# Example Company
## Key Features
## Company Information
One # for the title, ## for every section below it. That’s it. A single character difference – but the gap between a file that works and one that silently doesn’t.
Running your file through a dedicated LLMs.txt Validator takes minutes and catches exactly these issues. It’s the same logic as checking your sitemap for broken links – obvious hygiene that most teams skip.
Without validation, llms.txt is just good intentions. With it, it becomes an actual signal.
The gap is growing – and it doesn’t show in your dashboard
AI-generated answers now appear on a significant share of search results pages. Platforms like Perplexity are built entirely around synthesized responses. ChatGPT cites live content. The channel is growing faster than any that came before it.
What gets cited in those environments isn’t determined by a ranking algorithm. It’s determined by which content an AI can extract and represent accurately and confidently.
If your schema is vague, your llms.txt is unvalidated, and your entities aren’t explicitly defined – you’re invisible in that channel, and you won’t find out from your analytics.
What to actually do
You don’t need to rebuild your site. Start with this:
Audit for clarity, not just keywords. Can each major section of your key pages stand alone as a clear answer? Are entities named explicitly?
Fix your structured data with AI in mind. Schema types like FAQPage, HowTo, and Speakable are particularly useful for LLM extraction.
Validate your llms.txt. Don’t assume it works – check it.
Start manually testing AI visibility. Ask ChatGPT or Perplexity the questions your customers would ask. See what comes back. The absence of your brand is data too.
Being ranked and being cited are two different things now
For most of the internet’s history, SEO covered both. That’s no longer true.
The brands that show up in AI-generated answers are the ones whose content AI systems can confidently parse, extract, and attribute. That requires a different kind of optimization – and it’s still early enough that doing it well is a real differentiator.
The question isn’t just “how does Google see my site?” anymore.
It’s: how does an AI read me – and would it trust what I’m saying enough to quote it?
If you don’t know the answer, that’s where to start. Geordy.ai gives you the tools to find out – and to actually fix what’s getting in the way.