LLM SEO (Large Language Model SEO) is the practice of optimising your brand's digital presence so that AI language models like ChatGPT, Claude, Gemini and Perplexity recommend, cite and mention your brand positively in their responses.

LLMs learn from vast datasets of web content. Brands that appear frequently in high-authority sources, have clear entity profiles, demonstrate E-E-A-T signals and produce well-structured, factual content are more likely to be cited in AI responses.

Traditional SEO optimises for Google search rankings. LLM SEO optimises for AI-generated answers — focusing on brand mentions, entity clarity, E-E-A-T authority and content that AI models use as source material, rather than keyword rankings and backlinks alone.

Directly no — you cannot control LLM outputs. But indirectly yes: by building the authoritative content, entity presence and brand mentions that AI training and retrieval systems draw from, you increase the likelihood of positive citations over time.

We track brand citation frequency across major LLMs, sentiment of AI responses, share of voice vs. competitors in AI answers, and traditional authority metrics (backlinks, E-E-A-T signals) that correlate with LLM citation likelihood.

Building the authority signals that influence LLM citations typically takes 3–9 months. Quick wins — like improving structured data and brand consistency — can be implemented immediately, while earned PR and content authority build over time.

Yes. Wikipedia and Wikidata are among the most trusted sources in LLM training datasets. A Wikipedia presence, or at minimum a Wikidata entity entry, significantly strengthens your brand's LLM citation profile.

Yes. We always recommend running LLM SEO as part of a broader strategy that includes traditional SEO, AEO and GEO — our SEO·AEO·GEO·LLMO package combines all four layers into a unified search visibility strategy.