LLM SEO that shapes how AI assistants describe your brand

How is LLM SEO different from AI search optimization?

AI search optimization targets answers that cite web pages in real time; LLM SEO looks at the whole picture of what a model believes about your brand. That includes answers with no citations at all, where the model relies on what it absorbed during training.

Ask an assistant to describe your company without web search and it will answer from memory, sometimes with old prices, a former product name or a competitor's feature. Ask it with search on and it may cite a forum thread rather than your site. Our work covers both: the live sources answer engines retrieve, and the long-lived public record that future models are trained on.

How do you find out what LLMs say about your brand?

Run a structured set of brand and category prompts across ChatGPT, Claude, Gemini and Copilot, with web search on and off where the product allows, and record every factual claim. The point is to catch errors and omissions, not just count mentions.

We check claims against a fact sheet you approve: what you sell, who for, where, since when, at what price range and how you differ. Each wrong or missing fact gets a probable source, such as an old press release, a stale directory listing, a review site profile or a Wikipedia line, so the fix targets the cause.

• Branded prompts: who is the company, what does it sell, what does it cost.
• Category prompts: best options for a given need, with and without your name.
• Comparison prompts: you against the competitors buyers name most often.
• Accuracy log: each claim marked correct, outdated, wrong or missing.

How do you fix wrong information an AI assistant gives about your brand?

Correct the sources the model draws on, then make your own site the clearest statement of the facts. There is no form that edits a model's answer, so the work is upstream.

We update your About, pricing and product pages with plain, dated statements and Organization schema whose sameAs links point to your official profiles. We then correct the profiles you control, such as LinkedIn, G2 or Capterra, Google Business Profile and Yelp, and raise corrections on sites you do not control through their normal processes. Wikipedia and Wikidata have their own editing rules, and we follow them rather than editing around them. Live-search answers can reflect changes once pages are recrawled; training-based answers change only when newer models are released.

Why do brand mentions matter for LLM optimization?

Because models learn about brands from how the web talks about them, not only from links. Ahrefs' study of 75,000 brands found web mentions correlate 0.664 with AI Overview visibility, against 0.218 for backlinks.

That is a correlation, not proof of cause, but it matches what we see in prompt tests. Being discussed accurately on Reddit, YouTube, LinkedIn, industry publications and review sites gives models more consistent material to work from. Google's AI optimization guide advises against inauthentic mentions, and we hold to that: the aim is real coverage, expert commentary and original data others want to reference.

Should you block GPTBot, ClaudeBot and Google-Extended?

Only if keeping your content out of model training matters more than being represented in it. These are training controls, separate from the crawlers that power search answers.

OpenAI says blocking GPTBot only opts you out of training, and ChatGPT search relies on OAI-SearchBot. Anthropic separates ClaudeBot for training from Claude-SearchBot for search. Google-Extended governs Gemini training and Gemini Apps grounding but does not affect Google Search. Cloudflare's managed robots.txt can block several training tokens at once with a content signal of ai-train=no. For a brand that wants future models to know its products, allowing training crawlers on marketing pages can be reasonable, while gated or licensed content stays blocked. We set out the trade-off and configure the choice you make.

Where does llms.txt fit in?

It is optional hygiene, not a lever. Jeremy Howard of Answer.AI proposed the format in September 2024, and no major answer engine has confirmed using it; Google's John Mueller compared it to the meta keywords tag.

It does have a real use for developer documentation, which is why AI companies publish llms.txt files for their own docs: coding agents can read them. If you sell an API or developer product, we add one for your docs. For a brand site, the effort is better spent on accurate pages and consistent profiles.