LLMO is mostly a rebrand. The bit that isn't fits on a single page.
If that sounds like the sort of thing an agency says before charging you for a new service, fair enough. But hear it out. Because the honest version of the LLMO conversation is quite different from the version doing the rounds on LinkedIn, and getting the calm read first will save you a fair amount of noise.
You have probably seen the term in a newsletter, a vendor pitch, or a platform's marketing copy over the last few months. People use it with confidence. They do not always mean the same thing by it. Some use it interchangeably with GEO and AEO. Some use it to describe an entirely new discipline. Some are clearly just relabelling what they were already doing and hoping you will not notice.
LLMO, in plain English, means making your brand and content visible, accurate, and citable inside large language models, including ChatGPT, Claude, Perplexity, and Gemini. The core practice is real, it matters, and parts of it are genuinely new. Most of it, though, is the work that any well-run SEO and content programme should already be doing. Knowing which is which saves you from either ignoring something important or paying for something you already have.
This piece covers the definition, the LLMO versus GEO versus AEO confusion, what is actually new, what stays the same, and a practical read on whether your strategy needs to change.
What LLMO stands for, in one sentence
LLMO stands for Large Language Model Optimisation. It is the practice of making your brand, content, and named entities visible, accurate, and citable inside AI tools like ChatGPT, Claude, Perplexity, and Gemini.
Quick note before we go further: if you searched for LLMO and arrived here looking for pond and lake treatment products, Liquid Live Micro-Organisms is the other meaning of the acronym and a completely different field. This post is about the marketing meaning.
The clearest one-sentence version: LLMO is the practice of influencing what AI models say about your brand when someone asks them a question in your category. Everything else is either a tactic that serves that goal or a vendor adding complexity to justify a fee.
The term has been in circulation since roughly 2023, when it became clear that ChatGPT was being used as a research and recommendation tool rather than just a writing assistant. Marketers started asking whether you could influence the results, and whether doing so was different from what they were already doing for Google. The short answer is: yes, partly, and mostly no. An AI SEO agency focused on LLMO works across both layers: the traditional SEO base that feeds AI models their information, and the newer measurement and citation work that is specific to the LLM channel.
Why LLMO, GEO, and AEO are roughly the same thing
Three labels. One conversation.
GEO stands for Generative Engine Optimisation. The term entered marketing usage after academic research from Princeton, Georgia Tech, and The Allen Institute in 2023, and publications like Search Engine Land have tracked its evolution alongside LLMO and AEO as the AI search conversation has matured. GEO describes the practice of making content more visible inside generative AI tools. AEO stands for Answer Engine Optimisation, an older term that emerged around voice search and featured snippets, when marketers first noticed that some queries were answered directly rather than requiring a click.
All three terms describe variations of the same general practice: making your content the thing an AI tool reaches for when it answers a question in your category. There is no agreed industry definition that makes LLMO, GEO, and AEO genuinely separate disciplines today. They overlap heavily. Any agency or tool that presents them as clean, distinct services is drawing those lines themselves, not reflecting an established standard.
The most useful practical distinction, if you want one, is this. Use LLMO when you are talking specifically about the standalone LLM channel: ChatGPT, Claude, Perplexity, Gemini. Use GEO when you are talking about Google's AI Overviews, which draw on a live web search rather than a pre-trained model operating independently. Use AEO if you are talking about older answer-box and voice-search work, which predates the current AI search wave by several years. The terms help you say which channel you are working on. They do not describe three separate methodologies.
Why does any of this matter? Because AI search traffic converts at five times the rate of traditional search. The reader who arrives at your site via an AI recommendation has already been told you are relevant. They are verifying, not browsing. That conversion premium exists whether you call the underlying practice LLMO, GEO, or AEO. The label matters less than understanding the channel.
The bits of LLMO that are genuinely new
These are real, and worth understanding separately from the rebrand noise.
Brand visibility monitoring inside LLMs. Until recently, there was no systematic way to track whether your brand appeared in ChatGPT, Claude, Perplexity, or Gemini responses. Tools like Ahrefs Brand Radar, Profound, Otterly, and Peec now exist specifically for this purpose. This is a new measurement layer. It does not replace rank tracking or Search Console data, but it captures a channel that traditional SEO tools ignore entirely. ChatGPT alone reached hundreds of millions of weekly active users by early 2025, according to figures published by OpenAI. That is a large enough channel to take seriously.
Citation positioning. Getting ranked in Google and getting cited in an LLM are related but different goals. LLMs tend to lift content that is specific, well-structured, and phrased in a way that makes it easy to quote as a standalone answer. A page that addresses one question clearly, with named sources and concrete figures, is more citable than a page that addresses five questions vaguely. The structural choices that make a page liftable as a citation are worth thinking about deliberately, because they are slightly different from the choices that simply make a page rank.
The LLM as a discovery channel, not a publishing channel. You cannot put your content into ChatGPT directly. You can only influence what it says about you, by improving the content and entity coverage on your own site. This is a different mental model from paid search. You are not buying placement. You are building credibility that a model picks up when it retrieves or trains on content, and that distinction changes how you think about the work.
Prompt testing as a feedback loop. The LLMO equivalent of a rank tracker is asking an LLM a standard set of questions about your category and your brand every month, and recording what changes. There was no equivalent practice in traditional SEO, and it gives you signal about your LLM visibility before a client or prospect has already formed an opinion from an AI response.
A word on some overclaimed LLMO advice: you will see references to llms.txt files, vector database indexing, and new AI schema types. Some of this is legitimate experimentation. Most is not yet evidenced at scale. Treat any tactic presented as a guaranteed LLMO win with the same scepticism you would apply to a "guaranteed page one" pitch from 2012. Focus on the search visibility and traffic fundamentals first, then layer on the newer work.
The bits that are just SEO with a new label
This is the larger category. Most of what gets called LLMO is good content marketing, good technical SEO, or a combination of both.
Topical authority and entity coverage. LLMs favour brands that are thoroughly and accurately described across the web. Named clients, named team members, named tools, specific project outcomes. The more clearly and consistently your brand's identity appears in credible places, the more likely a model is to include you in a relevant answer. This is entity-based SEO, and it is not new.
Well-structured pages with one clear answer per section. Every H2 should address one question and address it completely. Paragraphs that wander, hedging that avoids commitment, content that says "it depends" six times without explaining what it depends on: all of this reduces citability. The SEO principle of clear, well-structured content has always applied. LLMO just gives you another reason to take it seriously.
Author bios, named clients, named tools, and citable statistics. Google's E-E-A-T guidelines have pushed content in this direction for several years: who wrote this, what experience backs it up, is it accurate. LLMs have arrived at the same preference independently. A page with a named author, a real biography, and specific references is more citable than one that could have been written by anyone.
Schema and structured data. FAQ schema, HowTo schema, organisation schema: these help both Google and LLMs understand what a page is about and pull structured answers from it. Not new work, but newly relevant to a wider range of destinations.
Strong internal linking and a coherent site architecture. An LLM does not crawl your site directly, but the web-scale training data it learns from rewards sites where pages connect logically and topical relationships are clear. The case for internal linking that SEOs have been making for a decade still stands.
The honest summary: if your brand has been doing core SEO work properly for the last twelve months, with entity coverage, well-structured content, and E-E-A-T signals in place, your LLMO base is already largely built. The genuinely new work sits on top of it, not underneath it.
How LLMO sits next to traditional SEO
The framing that makes most sense: traditional SEO drives the click. LLMO drives the recommendation.
Both still matter. They are not in competition. A brand that ranks well in Google gets the click when someone searches with intent. A brand that appears in an LLM recommendation gets the shortlist mention when someone asks an AI "who should I use for X?" Those are two separate moments in a buyer's research journey, and missing either one carries a cost.
Where it gets interesting is what an LLM citation actually does to behaviour. It often produces no click at all. The user asks ChatGPT for a recommendation, the model names three or four providers, and the user forms an impression without visiting any of their websites. That impression shapes which brand they search for later. The click comes, but it comes after the recommendation has already done its work. This is part of why AI-referred visitors convert at a significantly higher rate than traditional organic traffic: the person who clicks through from an AI response has already been pre-qualified by the model's answer.
Google AI Overviews sit slightly differently in this picture. They draw on a live web search rather than a pre-trained model's independent knowledge, which means they respond to SEO signals more directly. If your work targets Google AI Overview appearances specifically, GEO is the more accurate term. The line blurs in practice. But understanding the distinction helps you decide where to focus.
Do you need a new LLMO strategy, or not
The honest answer: probably not a new strategy, but possibly some additions to your existing one.
If your business depends on professional buyers researching providers, LLMO is worth your time. B2B services, consulting firms, SaaS platforms, healthcare organisations, professional practices: these are categories where buyers use AI tools to build their initial shortlists. If you are invisible in ChatGPT and Perplexity when someone asks "who provides X in the UK," you may be losing consideration before you get a chance to compete. That is worth addressing.
If your business depends on local intent and footfall, LLMO matters less. A plumber, a dental practice, a restaurant: these businesses still win through Google Business Profile, local map pack results, and local search terms. AI tools are not the primary research channel for "emergency plumber near me." Local SEO and your Google Business Profile remain the priority, and LLMO is a lower-priority addition rather than a replacement.
If your business depends on impulse retail at scale, LLMO matters least of all in the short term. The caveat is that brand mentions inside LLMs still influence awareness and consideration for brands people encounter repeatedly. But if your buyers are not asking ChatGPT for purchasing advice, this is not where your next marketing effort should go.
If you decide it is worth your time, the starting frame is three steps. First, audit your current LLM visibility: ask a standard set of prompts across ChatGPT, Claude, Perplexity, and Gemini and record whether and how your brand appears. Second, fix the obvious base: entity coverage, author bios, named clients, clear structured content. Third, test a small number of LLMO-specific moves, track monthly, and measure before adding more.
You do not need a new agency, a new tool, or a new budget to do steps one and two.
How to tell if LLMO work is actually working
The simple version: ask the same questions every month and record what changes.
You want two sets of prompts. The first is branded: six to ten questions that test direct brand recall. "What is [Your Brand]?", "What does [Your Brand] do?", "Is [Your Brand] a good option for [your main service]?", "What do people say about [Your Brand]?" You are testing whether the model knows who you are and whether what it says is accurate.
The second is category prompts: questions that test whether your brand surfaces when an LLM is asked for recommendations. "Who are the best [your service category] firms in the UK?", "Which agencies do [your type of work] well?", "What should I look for in a [your service] provider and who does it well?" Here you are testing competitive position, not just brand recall.
From those two sets, track three things over time. Presence: does your brand appear at all? Accuracy: is what the model says correct? Competitive position: who else appears alongside you, and where do you sit in the list?
Tools like Ahrefs Brand Radar, Profound, Otterly, and Peec can run this tracking at scale and surface trends automatically. A marketer running it manually with a spreadsheet and one focused hour a month will get credible signal. The tools add speed. They do not change what you are measuring.
One important caveat: LLM outputs vary between sessions. The same question asked twice can return different answers. The only meaningful unit of measurement is a monthly trend across a set of standardised prompts, not a single snapshot. A single favourable result tells you nothing. A consistent upward trend in presence and accuracy over three months tells you something real.
Free resource: AI Visibility Audit
Want to know where your brand currently stands inside ChatGPT, Claude, Perplexity, and Gemini? The AI Visibility Audit maps your current LLM presence, identifies accuracy gaps, and shows how your visibility compares to the competitors appearing in your category prompts. It is the diagnostic step before any LLMO work begins.
When to bring in an LLMO or AI SEO agency, and when not
Specialist help earns its place quickly in a few specific situations.
B2B businesses with a clearly defined buyer profile, complex services that buyers research over weeks or months, and regulated sectors where what an AI says about you carries real reputational weight: these are cases where LLMO work can produce meaningful results. A professional services firm that is completely absent from ChatGPT recommendations in its category is leaving warm consideration on the table. The fix often involves structural content work, entity coverage, and a monthly tracking programme, which a specialist can set up and maintain efficiently.
Brands that have run an LLM visibility audit and found the model saying something inaccurate or negative about them should get specialist input promptly. An AI tool that consistently misidentifies what you do, or credits your work to a competitor, is a reputational problem that compounds over time. The people who never click through to your site never get to see the correction.
On the other side: a small local business with strong local SEO already in place, a retail brand without a content foundation, or any brand that has not yet done the SEO basics has better places to spend first. LLMO without an SEO base is a layer built on sand.
The honest version of the CT position: most marketing teams should fix their AI-aware SEO base before bringing in a specialist. Our AI search agency service is built around that sequence, starting with the base and adding LLMO-specific work on top of it, not instead of it.
Where to start
Before you change your strategy, find out what you are actually working with.
The AI Visibility Audit maps your brand's current presence inside ChatGPT, Claude, Perplexity, and Gemini. It identifies where you appear, where you are absent, what is being said about you, and how your visibility compares to the competitors showing up in your category prompts. That picture is the right starting point for any LLMO work, whether you plan to manage it yourself or bring in help.
Free resource: AI Visibility Audit
The audit takes the guesswork out of step one. You find out whether the LLMO conversation is urgent for your business, or whether it can wait while you strengthen the foundation. Either answer is useful.
LLMO is a useful label for a real channel. It is not a panic button. The brands that will handle it well are the ones that audit first, fix the obvious gaps in their SEO and content foundation, and then decide how much specialist work they actually need. Most will need less than the vendor pitches suggest, and more than ignoring the channel entirely.
