LLM SEO: how to rank inside large language models
Search increasingly happens inside the model. Here's how to be the brand it names — and the source it cites.
What is LLM SEO?
LLM SEO is the practice of making your brand discoverable, citable and recommendable by large language models — ChatGPT, Claude, Gemini and the search engines built on them. Where classic SEO optimizes for a ranked list of links, LLM SEO optimizes for being part of the generated answer itself.
How LLMs surface brands
Models draw on two things: what they learned during training (your brand's footprint across the web) and what they retrieve live via web search and citations. To be named, your brand needs a clear, consistent presence in the sources models trust; to be cited, your domain needs to be reachable and quotable by AI crawlers.
Tactics that move the needle
Publish citable, factual, well-structured content; earn mentions on the third-party sources models already quote; keep your site crawlable by AI bots (GPTBot, ClaudeBot, PerplexityBot); and use clear entity signals so the model connects your brand to its category. The goal is simple: be the source the model reaches for.
Measuring LLM SEO with CiteLens
You can't optimize what you can't see. CiteLens queries your prompts across the major models, then reports two distinct signals — whether the model names your brand and whether it cites your domain — with a 95% confidence interval, benchmarked against competitors, plus an action list for closing the gap.