LLM SEO:Get Cited by ChatGPT, Gemini & AI Overviews

LLM SEO is the practice of optimizing your own website and content so AI models like ChatGPT, Gemini, Claude, and Perplexity understand, trust, and cite it when they answer questions. It is also called generative engine optimization (GEO). Where traditional SEO is measured in rankings and clicks, LLM SEO is measured in citations and mentions inside AI answers. In practice it covers answer-first content structure, structured data and entity consistency, E-E-A-T signals like named authors and authoritative sourcing, comparison content with transparent criteria, AI crawler access, and ongoing freshness, all verified with a monthly citation audit across the major models.

The on-site work a tracker cannot. A generative engine optimization agency audits how the major models answer the questions your buyers ask, finds the pages they cite instead of yours, and then changes your site so it becomes a better source: rewriting key pages answer-first, adding structured data, fixing entity and author signals, building honest comparison content, and making sure AI crawlers can reach all of it. Then it re-measures. Our generative engine optimization services follow the nine phases published on this page, and every engagement is scoped in writing after a free audit.

At the start, the citation audit (prompts tested, each answer word for word, whether you were named or cited, and who appeared instead) and a prioritized fix list. During the engagement, the rewritten pages, schema, and entity fixes named in your written scope, delivered as implemented changes or as a specification for your developers. Every month, a report that re-runs the same prompt panel and shows which answers changed, which did not, and what we recommend next.

They overlap, but the goal differs: traditional SEO optimizes for rankings and clicks in search results, while LLM SEO optimizes for citations and mentions inside AI-generated answers. Strong classic SEO helps, especially for models that search the web live, but it is not sufficient: AI answers can cite pages that do not rank on the first page of Google, because models weigh extractability, entity clarity, and trust signals differently than ranking algorithms do. A page can rank first and never get quoted, and a page on page three can be a model's favorite source.

They are the two halves of one AI search strategy. LLM SEO (or GEO) is the on-site half: optimizing the website and content you control so models understand, trust, and cite it. LLM seeding is the third-party half: building credible presence on the outside surfaces models trust, like communities, industry publications, comparison lists, and review platforms. Models draw on both your site and everyone else's when they compose an answer, so most engagements should include both. We run them together, and our LLM seeding page lays out the third-party playbook in the same detail as this one.

Yes, in substance. LLM SEO, LLM optimization, SEO for LLMs, and generative engine optimization (GEO) are different names for the same discipline: making your own content understandable, trustworthy, and citable to generative AI models. AI search optimization is the umbrella term for the whole program, on-site and off-site. AEO (answer engine optimization) is the adjacent layer that focuses on question-and-answer pages. The label matters less than the division of labor: LLM SEO is the on-site half, and LLM seeding is the third-party half.

For measurement, yes. LLM visibility trackers monitor how often models mention or cite you across a set of prompts, and the good ones are useful scoreboards. What a tracker does not do is restructure your pages, build your schema, fix your entity signals or earn citations. We are a done-for-you service, so we run the measurement (a monthly citation panel across ChatGPT, Gemini, Claude, and Perplexity) and the optimization work it points at. If you also want to know when AI says something inaccurate about you, that is our AI reputation monitoring service.

It depends on where each model gets its information. Answers built from live web retrieval (Perplexity, ChatGPT search, Google's AI results) read your site as it is now, so they tend to respond first once restructured or new pages are reindexed. Answers generated from a model's training data shift when the model is retrained on a newer snapshot of the web. We verify with a monthly panel and iterate on what moves.

Yes, and we will set it up. llms.txt is a proposed standard: a plain file at your site root that points LLMs to your most important content in a format that is easy for them to consume. The cost is near zero, so it is worth doing alongside the content structure, schema, trust signals, and freshness work described on this page.

If citations are the goal, let in the crawlers that feed AI answers. The main ones are OAI-SearchBot (the OpenAI crawler that surfaces sites in ChatGPT search) and GPTBot (OpenAI's separate training crawler), Claude-SearchBot and ClaudeBot (Anthropic's search and training crawlers), PerplexityBot, and Google-Extended, which is a robots.txt control over whether your content is used to train and ground Google's Gemini models and does not affect Google Search. Many sites block one or more of these by accident, through an overzealous robots.txt rule or a CDN bot filter. We audit exactly which of these can reach your key pages and fix the ones that cannot.

It starts with a free AI visibility audit, so you see your baseline before spending anything: how the major models answer your category questions today and whether they cite your site. From there, scope depends on the size of your site, how many pages need restructuring, how competitive the category is, and whether we are also running LLM seeding on the third-party side. It is typically a monthly engagement, quoted in writing before you commit, and we report against the citation panel every month so you can see exactly what you are paying for.