More buyers now ask AI assistants instead of searching. Our LLM SEO services, also called generative engine optimization, help your brand get recommended and cited inside ChatGPT, Perplexity, Gemini, and other LLMs.
A growing share of research and buying questions now go to LLM assistants like ChatGPT, Perplexity, and Gemini rather than a search box. When someone asks one of these tools to recommend a product or provider, you want to be the answer it gives.
LLM SEO, also known as generative engine optimization (GEO), is how you earn that. We strengthen the signals these models draw on, authoritative content, clear entity associations, and credible third-party citations, so your brand is surfaced, recommended, and cited inside AI assistants.
LLM SEO, or generative engine optimization (GEO), is the practice of optimizing your brand and content to be surfaced, recommended, and cited by large language model assistants such as ChatGPT, Perplexity, and Gemini.
Because these models draw on training data, the live web, and trusted sources, LLM SEO focuses on building strong entity signals, authoritative and well-structured content, and credible mentions and citations across the web, the inputs that shape what an AI assistant says about your category and recommends to its users.
We assess how ChatGPT, Perplexity, and Gemini currently describe and recommend in your category.
Strengthening the knowledge-graph and entity signals that tie your brand to its category.
Content designed to be the clearest, most citable source on your key topics.
Earning the third-party mentions and references that LLMs treat as trust signals.
Schema that helps machines understand your brand, products, and expertise.
Monitoring how often and how favorably LLMs surface you versus competitors.
If ChatGPT, Gemini, and Perplexity don't know you, you're invisible in AI answers. We build the authoritative, well-structured content and citations that get your brand recognised and recommended.
We analyse which sources AI engines pull from for your topics and earn the mentions, reviews, and structured data that make you a preferred, citable source.
LLMs favour clear, well-structured, factual content. We restructure pages with schema, clear headings, and direct answers so models can extract and quote them confidently.
Outdated or inaccurate brand information spreads fast through models. We correct the underlying sources and strengthen authoritative signals so assistants describe you accurately.
We monitor how often and how favourably the major models mention and cite your brand across key prompts, turning a fuzzy new channel into something you can actually track.
We combine classic SEO foundations with generative-engine optimisation, so you rank in Google and get cited by AI, capturing demand across both at once.
We test how leading assistants describe your category and whether they recommend you.
We strengthen entity signals and create the citable content LLMs prefer to draw on.
We earn the authoritative mentions across the web that shape what LLMs say.
We monitor your LLM share of voice and refine as models and answers change.
We help you win visibility inside AI assistants while most competitors are not even watching it.
We help you show up in LLM answers before your competitors realize it matters.
We build the entity and citation signals LLMs actually rely on.
We track how often AI assistants surface and recommend you over rivals.
Search is splitting in two. Alongside the familiar list of blue links, a fast-growing share of questions are now answered directly by large language models in ChatGPT, Gemini, Perplexity, and Google's AI Overviews. LLM SEO, sometimes called generative engine optimisation, is the practice of making sure your brand is the one those models surface and cite.
In AI answers, being cited as a source is the equivalent of ranking first. Models draw on content they consider authoritative, well-structured, and trustworthy. We engineer exactly those signals, clear answers, strong entities, schema, and credible mentions, so your brand becomes a source the models reach for.
LLMs reward content that is both authoritative and easy to parse. That means genuine expertise expressed in a machine-readable way: direct answers, logical structure, consistent facts, and corroborating sources across the web. We work on your content and your wider digital footprint so both align.
Most competitors are not optimising for AI search yet, which makes it a rare early-mover advantage. We track how the major models talk about you and your category, then act on the gaps, so you build visibility in this channel before it becomes as crowded as traditional search.
They overlap. AI SEO often focuses on Google's AI Overviews; LLM SEO (GEO) focuses on standalone assistants like ChatGPT, Perplexity, and Gemini. We cover both.
You cannot control it, but you can strongly influence it by improving the entity signals, authoritative content, and citations these models rely on, which is exactly what we do.
We track your share of voice in LLM answers, how often and how favorably assistants surface and recommend you in your category versus competitors.
Yes. Assistant usage is growing fast and the brands building visibility early are establishing an advantage that is hard for latecomers to displace.
LLM SEO (also called generative engine optimization) is the practice of making your brand visible inside answers from large language models like ChatGPT, Claude, Gemini, and Perplexity. Instead of ranking links, the goal is to be named, described accurately, and cited when these tools answer questions in your category.
Traditional SEO earns rankings in a list of links; LLM SEO earns mentions and citations inside a single synthesised answer. It leans more on entity clarity, structured facts, third-party corroboration, and freshness than on backlinks alone. The two reinforce each other and we run them together.
We cover the major surfaces: ChatGPT and SearchGPT, Google AI Overviews and AI Mode, Perplexity, and Claude, plus emerging answer engines. The underlying playbook is shared, with platform-specific tuning for how each model retrieves and cites sources.
We define a set of buyer-intent prompts in your category and test them on a schedule, recording whether you are mentioned, how accurately you are described, whether you are cited with a link, and which competitors appear. That gives a clear share-of-answer benchmark over time.
Frequently, yes. Wrong answers usually come from outdated or conflicting sources online. We identify those sources, correct or out-publish them, strengthen your own structured data and authoritative content, and reinforce the accurate facts until the models repeat them.