LARGE LANGUAGE MODEL OPTIMISATION
Somewhere right now, an AI is describing your business to a potential customer. Is it getting it right?
Every day, more people are turning to ChatGPT, Claude, Gemini, and Perplexity to research products, compare services, and ask for recommendations, often well before they ever type a query into Google.
When that happens, the language model doesn't show your website. It generates an answer. This pulls together what it knows (or thinks it knows) about your brand, your competitors, and the topic at hand, and presenting that as a confident, conversational response.
If the model has a clear, accurate, well-sourced understanding of your business, that answer can put you directly in front of a ready-to-act customer. If it doesn't, if it's working from outdated information, thin sourcing, or simply doesn't know you exist. In this case, you don't get low visibility. You get left out of the conversation entirely.
LLM SEO is the discipline of closing that gap: making sure language models can find you, understand you correctly, and choose to bring you into the answer.
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What is LLM SEO?
LLM SEO is the practice of optimising how large language models including ChatGPT, Claude, Gemini, Perplexity, Grok and similar AI systems discover, interpret, store, and reference information about your brand.
It's the most technically demanding of the AI search disciplines, because it requires understanding not just how these models retrieve information, but how they form an internal "picture" of who you are, and what makes them confident enough to surface that picture in a conversational answer.
That involves a combination of:
Retrieval
making sure the model (or the tools it uses to look things up) can actually access accurate, current information about your business.
Interpretation
structuring that information so the model can understand it correctly, rather than guessing or generalising.
Reference
building the kind of authority and consistency that gives a model confidence to mention you by name in its answers.
Get all three right, and you give yourself a genuine chance of being part of the recommendations, comparisons, and explanations these tools generate every day.
STRUCTURAL DIFFERENCES
How LLM SEO differs from SEO and GEO
It's worth being precise about this, because the three terms get blurred together constantly. That leads to confusion and brands to focus on the wrong thing for them.
Traditional SEO
Traditional SEO is about ranking your website within a search engine's results pages.
GEO (Generative Engine Optimisation)
GEO is about being cited and drawn upon within generative answer experiences that sit inside the search journey. Most notably Google's AI Overviews.
SEE OUR GEO SEO SERVICES →LLM SEO
LLM SEO is about how standalone conversational AI platforms ChatGPT, Claude, Gemini, Perplexity, Grok, discover, interpret, and reference your brand, often in contexts that have nothing to do with a traditional search engine at all.
The practical difference matters: someone asking ChatGPT "which agencies specialise in X" is having a fundamentally different interaction than someone typing the same query into Google. LLM SEO is about winning that conversation specifically on its own terms, not as an extension of traditional search thinking.
THE BUSINESS CASE
Why LLM visibility matters now
This isn't a niche, future concern. It's already shaping real buying decisions.
Conversational AI tools are increasingly used as a first step in research and comparison, particularly for considered B2B and professional service decisions, where trust and credibility carry real weight.
When someone asks an LLM for a recommendation, they typically receive a small, confident shortlist and not ten blue links to weigh up themselves. Being on that shortlist (or not) can be the entire ballgame.
These models are trained and updated on cycles that mean today’s gaps in how they understand your brand could persist for a long time if they aren’t addressed deliberately.
Early movers in any new search channel tend to build a lasting advantage. The brands that get their LLM visibility right now are the ones likely to be entrenched as "the obvious answer" once the channel matures.
AI DATA EXTRACTION
How LLMs find and use brand information
To optimise for language models, it helps to understand how they form an understanding of your brand in the first place. Broadly, this comes from a combination of:
Training data
the broad base of text the model has learned from, which shapes its general "knowledge" of your industry, your competitors, and (if you’re lucky) your brand.
Retrieval-augmented sources
live lookups many AI tools perform when generating an answer, drawing on current web content, structured data, and trusted reference sources.
Structured signals
files, markup, and content patterns (such as llms.txt, schema, and clearly structured pages) that help models quickly and accurately interpret what your site contains.
External authority and mentions
how consistently and credibly your brand is discussed across the web, which shapes how confident a model is in referencing you.
The practical implication: you can't influence all of these equally, or instantly, but you can deliberately strengthen the ones within your control, and that's where LLM SEO concentrates its effort.
ENGINEERING APPROACH
Technical foundations for LLM Visibility
This is where LLM SEO becomes genuinely technical and where we believe most agencies talk in vague terms rather than getting into the detail that actually moves the needle. Here's what we focus on in practice:
Content architecture & retrieval readiness
We assess and restructure how your content is organised, making sure the pages that should act as canonical sources for key topics are clear, comprehensive, and easy for a model (or the tools it uses to retrieve information) to access and parse correctly.
Source clarity & canonical pages
We work to establish clear, authoritative pages for each of your core topics and services. This reduces the risk of a model piecing together a fragmented or inconsistent picture of your business from scattered, overlapping content.
Entity consistency
We audit and align how your brand, your people, your services, and your positioning are described across your site and beyond. This is because inconsistency is one of the most common reasons models misrepresent or fail to confidently reference a brand.
llms.txt review and implementation
We assess whether an llms.txt file is right for your site, and if so, implement and refine it, giving language models a clearer signal about which parts of your site represent your most authoritative, canonical information.
Semantic content restructuring
We restructure existing content so its meaning and relationships are clearer to a model. Using clearer headings, more direct language, well-defined terms, and logical content hierarchies that reduce ambiguity.
Structured FAQ & answer block design
We build genuinely useful FAQ and structured answer sections, written in the natural, conversational language people actually use when prompting AI tools, not stiff, search-engine-era keyword phrasing.
CONTENT STRATEGY
Examples of page types that help LLM visibility
Certain page types consistently help models build a clearer, more confident picture of a brand. We typically prioritise:
clear explanations of what specific terms, services, or concepts mean in your context.
structured, genuinely useful comparisons that help a model (and a person) understand how options differ.
detailed, well-organised pages that leave little room for a model to guess or generalise.
clear information about the real people behind your content and expertise, supporting trust and attribution.
transparency about how you produce and verify your content, which can support a model’s confidence in citing you.
authoritative reference points for terminology within your field.
OUR CAPABILITIES
Our LLM Optimisation services
Semantic content restructuring
Reorganising and rewriting existing content so its structure and meaning are unambiguous to a language model, without losing the tone and personality that makes it work for human readers too.
Entity and topic graph mapping
Mapping how your brand, services, and topics relate to one another and to the wider landscape, so models can build an accurate, joined-up picture rather than a fragmented one.
llms.txt review and implementation
Assessing, building, and refining llms.txt files that help language models understand which parts of your site represent your clearest, most authoritative information.
Structured FAQ / answer block design
Designing FAQ and Q&A content that mirrors the natural, conversational way people actually prompt AI tools, increasing the chance your content matches real queries closely enough to be drawn upon.
Conversational query and prompt research
Researching the actual prompts and conversational queries people use when asking AI tools about your industry, and using that insight to shape the topics, language, and structure of your content.
Brand mention / community mention strategy
Building genuine mentions and discussions of your brand across the communities, forums, and platforms that contribute to how language models build their understanding of who's credible in your space.
Knowledge source expansion
Implementing clear, accurate schema markup and structured data across your site to help models quickly understand entities, relationships, and key facts on your pages.
Digital PR / expert citation strategy
Establishing regular review and update processes so your content remains current and authoritative in the eyes of language models that favour recent, well-maintained information.
Monitoring across key AI Interfaces
Setting up ways to track how your content performs in AI answers over time and using those insights to continuously refine and improve your overall approach.
OUR METHODOLOGY
Our Implementation Framework
Audit
We test how ChatGPT, Claude, Gemini, and Perplexity currently describe and recommend your brand, and identify where the gaps, inaccuracies, and opportunities sit.
Entity & topic mapping
We map how clearly and consistently your brand, services, and authority are represented across your site and the wider web.
Technical foundations
We address content architecture, canonical source pages, llms.txt, and entity consistency, the groundwork that makes accurate understanding possible.
Content & semantic structuring
We create and restructure content designed to be unambiguous, well-organised, and aligned with how people actually prompt AI tools.
Authority & mentions
We build genuine citations, expert mentions, and credible third-party coverage that strengthen a model’s confidence in referencing you.
Monitoring & reporting
We track how your representation and visibility change across each platform over time, and refine the approach based on what’s actually shifting the picture.
PERFORMANCE COMPARISON
LLM Optimisation vs Traditional SEO
| METRICS | TRADITIONAL SEO | LLM OPTIMISATION |
|---|---|---|
| Where it plays out | Search engine results pages | Conversational AI platforms (ChatGPT, Claude, Gemini, Perplexity) |
| What "winning" looks like | A high ranking position | Being accurately and confidently included in a generated answer or recommendation |
| Core mechanics | Keywords, on-page optimisation, backlinks | Entity clarity, semantic structure, retrieval readiness, authority and consistency signals |
| The user's experience | Browses a list of results and chooses | Receives a generated answer, often a single recommendation or short shortlist |
SPECIALISM SEPARATION
LLM Optimisation vs GEO
These two disciplines are closely related, and many of the same foundations support both, but they focus on different environments:
GEO Environmental Focus
GEO is concerned with visibility inside generative search experiences that sit within the traditional search journey, most notably Google's AI Overviews.
LLM Optimisation Environmental Focus
LLM Optimisation is concerned with visibility inside standalone conversational AI platforms (ChatGPT, Claude, Gemini, Perplexity) where someone may never touch a traditional search engine.
FREQUENTLY ASKED QUESTIONS
