LLM Optimisation Services · UK

LLMs like ChatGPT and Claude learn from massive datasets scraped from the web. During training, they absorb patterns from billions of web pages — including your website, review sites, directories, press articles, forums, and social platforms. This creates an internal representation of your business: what you do, where you are, how credible you are, and what topics you’re associated with.

The model doesn’t store a database entry for your brand. It builds a probabilistic understanding based on how consistently and prominently your entity appears across trusted sources. If the data is thin or contradictory, the model’s representation is weak, and it either describes you poorly or doesn’t mention you at all.

LLMs have two ways of knowing about your brand. First, their training data — the web content they absorbed during their last training run. This is static and can be months or years old. Second, retrieval-augmented generation (RAG) — where the model searches the web in real time before answering, like ChatGPT’s search feature or Perplexity.

LLMO targets both. For training data, the goal is consistent, prominent entity presence across authoritative sources so the next training run strengthens the model’s representation. For real-time retrieval, the goal is structured, extractable content that the model can find and cite when answering live queries.

Traditional SEO optimises individual pages for specific keywords. LLMs don’t think in keywords — they build entity-level understanding. If your Google Business Profile says you’re a “digital marketing agency”, your website says “marketing consultancy”, and a directory lists you as a “web design firm”, the model’s confidence in your entity is fragmented.

LLMO prioritises entity consistency: making sure your business is described the same way, with the same core claims, across every platform these models learn from. This gives the model a clear, confident picture it’s willing to repeat and recommend.

LLMO is the foundational layer. It focuses on the model’s core understanding of your brand. GEO (generative engine optimisation) targets visibility in generative search engines. AEO (answer engine optimisation) targets tools like Perplexity. AIO (AI Overview optimisation) targets Google’s AI summaries.

They all draw on the same foundation: if the model’s entity-level understanding of your brand is strong, you’re more likely to appear across all of these surfaces. LLMO is where that foundation gets built.

Schema.org markup provides machine-readable data about your business that LLMs can process directly. Organisation schema tells the model your name, address, and founding details. Service schema explains what you offer. FAQ schema provides extractable question-and-answer pairs. SameAs links connect your entity across platforms.

This structured data doesn’t replace good content, but it gives models an unambiguous, fact-checkable layer of information about your entity. It’s particularly valuable for retrieval-augmented systems that scan your pages before generating an answer.

LLM optimisation (LLMO) is the practice of shaping how large language models like ChatGPT, Claude, and Gemini understand and represent your business. The goal is making sure these models describe you accurately and recommend you for the right queries.

SEO focuses on ranking your pages in Google’s search results. LLMO focuses on the model’s entity-level understanding of your brand — how it describes you, whether it recommends you, and how confidently it does so. Good SEO helps because well-ranked content is more likely to be in training data, but the signals that matter for LLMs are different.

Not directly — nobody can edit a language model’s outputs. But you can shape the evidence the model draws from. If the web paints a clear, consistent, well-corroborated picture of your brand, the model’s representation will reflect that. LLMO is about making the evidence as strong as possible.

It depends on which models and which features. Real-time retrieval tools (ChatGPT’s search, Perplexity) can reflect changes within weeks. The model’s core training data takes longer — improvements compound over two to six months as entity data strengthens. We’ll give you realistic expectations based on your starting point.

Good SEO is a helpful foundation, but it’s not enough. SEO optimises for Google’s ranking algorithm. LLMO optimises for how language models understand your entity across every source they learn from — not just your website. As more people use AI assistants for research and recommendations, the gap between “ranks well on Google” and “recommended by AI” will widen.

It depends on scope — how much entity clean-up is needed, how many sources need attention, and how competitive your space is. We’ll give you a clear quote after an initial brand perception audit. LLMO often works best as part of a broader AI SEO strategy.

Because you get the senior specialists doing the work, not an account manager relaying messages. We're a small, senior team: strategy, execution and reporting come from the people who own the result, decisions happen fast, and nothing falls through the gaps between departments. If that sounds like a better fit, get in touch or see how pricing works.