In short
LLM SEO is the practice of making large language models understand and cite your brand inside their answers. It combines entity clarity (consistent identity across the web), structured facts (schema, Wikidata, sameAs links), authority signals (third-party citations) and AI-friendly content formats (direct answers, FAQs, defined terms).
How language models build their picture of your brand
An LLM forms its picture of your brand from two sources: the data it was trained on (which lags by months or years), and the data it retrieves in real time when answering a query. Both reward the same things — consistent identity, structured facts, and authoritative third-party mentions.
- Consistent name, address, role across the web
- Wikidata and Wikipedia presence where appropriate
- Verified profiles on LinkedIn, Crunchbase, industry directories
- schema.org Organization with sameAs links
- Citations from media, podcasts, expert quotes and original data
What LLM SEO is not
It's not gaming a prompt. It's not stuffing pages with 'as an AI'. Models are increasingly good at filtering manipulation, and being delisted from LLM answers is far more damaging than being delisted from search.
The LLM SEO workflow
Audit your brand presence inside ChatGPT, Claude, Gemini, Perplexity and Copilot for 30+ buyer prompts. Identify where competitors are cited and you aren't. Fix entity signals (schema, sameAs, About page, author bylines). Earn third-party citations through digital PR, original data and expert quotes. Track monthly.
Measuring LLM SEO
Citation share of voice per engine, branded prompt visibility, AI-referral traffic, and downstream pipeline. The qualitative signal — buyers telling you 'I found you through ChatGPT' — is increasingly common and worth logging.
