LLM SEO is a colloquial term for the practice of influencing how large language models represent your brand in their outputs. It overlaps heavily with AEO and GEO, but the “SEO” framing is useful because it signals something most founders already understand: there is a ranking to be won inside AI systems, just as there is in Google Search.
Why “LLM SEO” is a useful framing
Traditional SEO has a clear mental model: Google ranks documents, and your job is to make your document rank higher. LLM SEO extends that model: language models synthesize answers from many sources, and your job is to make your brand show up favorably in that synthesis.
The framing helps founders who already think in SEO terms get up to speed quickly. Many of the underlying instincts transfer: you want to be cited by authoritative sources, you want your positioning to be clear and consistent, and you want to create content that directly answers the questions your users ask.
What is different from traditional SEO
The mechanisms are different in important ways:
No explicit ranking signal. Google tells you approximately where you rank. Language models do not expose a ranking. You infer your position by running prompts and observing the outputs - which is exactly what tools like AskAiRank automate.
Training data matters, not just crawl-time data. A model like GPT-4 was trained on a snapshot of the web. If your brand had good coverage in that training snapshot, you start with an advantage. Newer content may take longer to influence a model’s outputs unless the model has web retrieval capabilities (like Perplexity).
Entities matter more than keywords. LLMs think in entities - named brands, people, products, concepts - rather than in keyword-match patterns. Being consistently named as an entity across diverse, authoritative sources is more valuable than keyword density.
Multiple models, multiple battlegrounds. Google SEO has one main search engine to optimize for (with some Bing). LLM SEO has four major models (ChatGPT, Claude, Perplexity, Gemini) plus Google AI Overviews, and each can behave differently. Your brand may appear prominently in one and be entirely absent in another.
Practical LLM SEO for indie founders
The high-leverage activities for indie founders are:
- Directory presence: get listed accurately on the platforms AI models draw from heavily (G2, Product Hunt, Capterra, Crunchbase, GitHub if relevant).
- Consistent positioning: make sure every public mention of your product uses the same core description of what it does and who it is for.
- Answer-format content: write blog posts and documentation that directly answer the questions AI models are likely to surface about your category.
- llms.txt: publish a plain-text summary of your product at
/llms.txtto help AI crawlers understand your site. - Measure and iterate: use prompt tracking to find out which prompts show you and which do not, then address the gaps.
LLM SEO is still a young discipline. The practitioners who invest in measurement and structured experimentation now will have a significant head start over those who wait for the field to mature.
