What is LLM SEO?
LLM SEO is the practice of improving how large language models understand, remember and recommend your brand. Large language models, or LLMs, are the AI systems behind assistants such as ChatGPT, Gemini, Claude, Copilot and Meta AI. They answer questions using a mix of knowledge learned during training and information retrieved from the web at the time of the question.
LLM SEO works on both sources. It shapes the wider web content that models learn from, and it makes your pages easy to retrieve and cite when a model searches.
Training knowledge vs retrieval
During training, a model reads a very large amount of text and learns patterns about entities, including brands, products and people. What it learns depends on how often and how consistently your brand is described across the sources it was trained on. This knowledge is fixed until the model is updated. LLM SEO is measured in mentions, citations and accuracy rather than rankings alone.
Retrieval is different. When a model searches the web, it can read your latest pages. Retrieval changes quickly, while training knowledge changes slowly. A complete LLM SEO plan addresses both timelines.
How LLMs form a picture of your brand
Models learn associations between words and concepts. If many trusted sources describe your company as "a payroll platform for restaurants", the model links your brand with payroll and restaurants. If sources disagree or rarely mention you, the association is weak and the model may not mention you at all.
This is why consistency and repetition across credible sources matter so much in LLM SEO.
Sources that carry weight in LLM SEO
Training data typically includes large public web crawls, encyclopedic sources, books, news, academic papers, code repositories and forums. Within these, well-established and frequently cited sources tend to have more influence than small, isolated pages.
We identify which sources describe your category most often and focus on earning accurate mentions there.
Co-occurrence and category association
Co-occurrence means your brand appearing in the same text as the topics you want to be known for. A brand mentioned repeatedly alongside "project management for architects" is more likely to be recalled when someone asks about that topic.
We plan PR, guest content, podcasts, reviews and partnerships that place your brand next to your target category terms in credible contexts.
Entity foundations for LLM optimization
Models and search engines both rely on entity data. Clear, consistent facts about your company name, category, founders, locations, products and history reduce confusion with similarly named brands and improve recall accuracy.
We align these facts across your site, Organization schema, LinkedIn, Crunchbase, industry directories and, where notability allows, Wikidata and Wikipedia.
Wikipedia and knowledge bases
Encyclopedic sources are commonly used in training and are trusted by retrieval systems. A neutral, well-sourced Wikipedia article can strongly shape how models describe a company, but only notable organizations meet Wikipedia's standards, and articles must follow strict neutrality rules.
We advise on notability honestly and focus on knowledge bases and structured sources that are appropriate for your business.
Retrieval-ready content for LLM SEO
When models search, retrieval systems split pages into chunks and select the most relevant ones. Sections that are self-contained, open with a clear statement and include specific facts are more likely to be selected and cited.
We restructure priority pages so each section answers one question completely, without depending on surrounding text.
Training crawler decisions
Crawlers such as GPTBot, ClaudeBot, Google-Extended and CCBot relate to training or dataset collection, while others such as OAI-SearchBot and PerplexityBot relate to retrieval. Blocking training crawlers may limit how future models learn about you, while blocking retrieval crawlers removes you from live answers.
We explain the trade-offs clearly and configure access to match your goals.
Correcting outdated LLM knowledge
Because training knowledge is fixed until a model is updated, outdated facts can persist. The fastest fix is usually through retrieval: publishing clear, current information that models find when they search. Over time, updated sources also shape future training.
We trace wrong facts to their sources, correct what can be corrected and publish authoritative updates.
Open-weight models
Models such as Llama, Mistral and others are used inside many business tools, chatbots and apps. Their knowledge also comes from large web datasets, so the same principles of consistent, credible presence apply.
LLM SEO therefore improves visibility not only in major assistants but also in the many products built on these models.
Measuring LLM visibility
We measure two things. Recall: what models say about your brand and category without searching. Retrieval: which of your pages are cited when models search. Both are tracked across several models with repeated prompts to account for variation.
Reports show recall accuracy, share of voice, citations and sentiment by model over time.
LLM SEO for SaaS and B2B
Software and B2B brands benefit from documentation, comparison pages, analyst coverage, review platform listings and technical communities such as GitHub and Stack Overflow, which are common training sources.
uRecruits now has 116 pages cited by AI platforms after feature and comparison pages were optimized.
LLM SEO for eCommerce
Product brands are recalled when they are consistently described in reviews, buying guides, comparison articles and forums. Retrieval-ready product and category pages help models cite you when they search.
McGrocer's catalog content now has 16.9K pages cited in AI search.
LLM SEO for local and service businesses
Local businesses are recalled less from training and more from retrieval, reviews and listings. Strong local profiles, reviews and local media mentions drive most LLM visibility here.
We combine local SEO with retrieval optimization for service businesses.
Choosing an LLM SEO agency
Ask agencies how they separate training recall from retrieval, which models they test, how they handle variation and what sources they target. Be cautious of claims to directly edit model knowledge, which is not possible.
Top SEO Agency shares its prompts, methods and results by model each month.
LLM SEO and wider AI search
LLM SEO overlaps with GEO, AEO and AI SEO. Together they cover what models know, what they retrieve and how they present answers.
Top SEO Agency plans all of these as one AI search strategy built on strong SEO foundations.
LLM SEO for brand accuracy
Language models sometimes describe brands with outdated or wrong facts. Consistent details on your website, Wikipedia style sources, directories and press help correct these mistakes over time.
We test brand prompts regularly and note any errors to fix at the source.
LLM SEO and training data versus live search
Some answers come from what a model learned in training, others from live web search. Training data changes slowly, while live search reflects current pages. We work on both: lasting mentions on trusted sites and crawlable, clear pages for live retrieval.
Prompt testing for LLM SEO
We build a fixed set of prompts that real buyers might ask, from brand questions to category comparisons, and run them across major models each month. Changes in mentions and descriptions show which work is paying off.
LLM SEO for product and pricing pages
When buyers ask AI tools about pricing, features or plans, models look for clear, current product pages. Plain pricing tables, feature lists and up to date plan names help models answer accurately instead of guessing.
