Emerging category
LLM SEO — Optimising Your Brand
for Large Language Models
When someone asks ChatGPT, Claude, or Gemini to recommend a product, tool, or company in your category, does your brand appear? LLM SEO is the discipline of ensuring it does — by understanding exactly how large language models learn about, remember, and surface brands.
Check your LLM visibility freeHow it works
How LLMs learn about brands
Large language models acquire brand knowledge through three distinct mechanisms. Each has different implications for how you can influence your representation.
Static knowledge
Pre-training data
During training, LLMs ingest hundreds of billions of tokens of web content. Brands that appear frequently in authoritative sources — Wikipedia, reputable news sites, industry publications, academic papers — are baked into the model's weights. This knowledge is static until the model is retrained.
Dynamic retrieval
Retrieval-Augmented Generation (RAG)
Modern LLM deployments add a retrieval layer: before answering, the model fetches fresh web pages. ChatGPT Browse, Perplexity, and Claude with web access all use RAG. Your content's freshness, structure, and crawl accessibility matter enormously here.
Curated shaping
Fine-tuning & RLHF
Some models are fine-tuned on curated datasets and shaped by human feedback. Brands that appear in high-quality Q&A datasets, helpfulness evaluations, or instruction-following examples are reinforced as credible sources.
Ranking factors
What signals matter for LLM visibility
Unlike traditional SEO where Google's algorithm weighs hundreds of inputs, LLM visibility comes down to a smaller, more learnable set of signals. Master these and you move from invisible to recommended.
Entity consistency
Your brand must be represented identically across your own site, Wikipedia, Wikidata, Crunchbase, LinkedIn, and every press mention. LLMs triangulate facts — if your founding year, HQ location, or product description differs across sources, the model may hedge or omit you entirely.
Factual density
Pages stuffed with marketing fluff score poorly. Pages with specific, citable facts — launch dates, customer counts, benchmark scores, named integrations — give LLMs precise information to quote. Write every page as if it will be retrieved verbatim.
Authoritative third-party coverage
LLMs heavily weight third-party corroboration. Reviews on G2 and Capterra, coverage in TechCrunch or Product Hunt, citations in industry reports — all of these amplify your presence in model outputs far more than your own site can alone.
Schema and structured data
JSON-LD Organization schema, SoftwareApplication schema, and FAQ schema give LLMs a machine-readable summary of your brand. Models with retrieval layers can ingest this directly, bypassing the need to parse prose.
Content freshness
For LLMs using RAG, freshness matters. Pages with a recent dateModified in their schema, regularly updated blog content, and active press coverage are more likely to be retrieved in response to current queries.
Category and comparison presence
LLMs learn category structure from 'best X' and 'X vs Y' pages. If your brand consistently appears in category listicles, comparison articles, and buyer's guides, the model learns to associate you with that category.
Measurement
How to measure LLM visibility
You cannot check Google Search Console for LLM traffic. Measuring LLM visibility requires a different approach: systematic prompt testing across models, tracked over time.
Key metrics
Presence rate
The percentage of your tracked prompts in which your brand is mentioned at all. A presence rate of 60% means you appear in 6 out of 10 relevant queries.
Position score
Where in the response your brand appears. A first-mention position scores higher than a trailing mention. Early position correlates with being the model's primary recommendation.
Visibility score
A 0–100 composite of presence rate and position, weighted across all tracked prompts. Your single headline number for LLM visibility health.
Share of voice
Your mentions as a percentage of all brand mentions across the same prompts. Tracks your competitive position within the AI conversation.
Per-platform breakdown
Scores segmented by ChatGPT, Claude, Gemini, and Perplexity — because each model has different training data and retrieval behaviour.
Surfaceable's LLM tracking
Surfaceable runs your configured prompts against live instances of ChatGPT, Claude, Gemini, and Perplexity on a scheduled cadence. Results are scored, stored, and trended so you can see whether your LLM SEO efforts are working.
- Configure prompts your customers actually ask
- Automated runs: daily, 3×/week, or weekly
- Full response logging for qualitative review
- Competitor tracking alongside your brand
- Trend charts and score history
- Alerts when your visibility drops
See how you rank inside every major LLM.
Free to start. Tracks ChatGPT, Claude, Gemini, and Perplexity.
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