LLM SEO
LLM SEO Services
LLM SEO is the narrow version of the problem: getting a language model to name your business when someone asks it a question in your category.
It is worth treating separately from search because the mechanism is different. A search engine retrieves and ranks documents. A language model composes an answer from a mixture of what it learned during training and what it retrieves at the moment of asking. Those two halves respond to completely different inputs, and only one of them can be influenced quickly.
Understanding which half is answering your category question is the first thing this engagement establishes, because it decides whether the work takes weeks or quarters.
Retrieval and memory are different problems
When a model answers with a live citation, it retrieved something. That is the tractable half. Your page can be made more retrievable and more quotable, and the change shows up reasonably quickly.
When a model answers with no citation and states something confidently, it is answering from what it absorbed during training. Nobody can edit that directly. What shifts it, slowly, is the weight of consistent information across the sources these models learn from.
The practical test is simple: ask the question and look for citations. A cited answer means work on structure and retrievability. An uncited answer means the slower corroboration programme, and it is honest to say so before taking the engagement rather than after.
What actually moves a model's answer
Three inputs do most of the work, in rough order of speed.
Retrievable structure. A page that answers the exact question, near the top, in a passage that stands alone without surrounding context. This is fast.
Repetition across independent sources. The same fact, stated consistently, in places the model reads: industry directories, reputable profiles, publications in your category, and communities where your category is discussed. This is medium speed and compounds.
Entity clarity. One unambiguous business, with a consistent name and description, so the model does not blend you with a similarly named company. Businesses with common founder names or generic brand names suffer here more than they realise.
The honest limits
Three things are worth saying plainly, because the category is full of claims that do not survive contact with how these systems work.
Nobody can guarantee placement in a model's answer. Retrieval and ranking behaviour changes without notice and without a changelog.
Nobody can edit training data. Services that imply otherwise are describing corroboration work in stronger language than it deserves.
Results vary between identical questions asked twice, which is why single anecdotes are useless and a fixed question set run repeatedly is the only sane way to measure anything here.
What a realistic timeline looks like
Retrieval work shows first. Restructuring a page that is already indexed so its passages stand alone can change a cited answer within a few weeks, sometimes faster on the surfaces that retrieve aggressively.
Entity disambiguation shows next, usually in a month or two. Once the naming and markup resolve cleanly, models stop blending you with a similarly named business and the answers get more consistent.
Corroboration is the slow one and it is measured in quarters, because it depends on third parties publishing and on models re-reading. It is also the most durable, since a fact repeated across many independent sources does not stop being true when a competitor updates their site.
Anyone offering a fixed timeline for placement inside a model answer is describing a hope. What can be committed to is the work and the measurement cadence.
The uncomfortable question worth asking first
Before commissioning any of this, ask what a model would have to read to conclude that your business is the right answer. If the honest response is that nothing distinguishes you from four competitors, the constraint is positioning rather than optimisation.
That is a real finding and it comes up often enough to mention here. Structural work makes a distinctive business easier to find. It does not manufacture a distinction that is not there, and an engagement built on the assumption that it will is one that disappoints both sides.
What to do next
Run three of your own category questions through ChatGPT and Perplexity before you do anything else. Look at whether you are named, and whether the answer carries citations.
Cited and absent means retrieval work, which is the faster route. Uncited and absent means the longer corroboration programme, and you should know that before committing to a timeline.
That five-minute test tells you more about the size of the job than any proposal will, and it costs nothing. Bring the result to the first conversation.
What the engagement includes
- A retrieval versus memory diagnosis. Which half of the model is answering your category questions, established before any work is scoped, because it determines the timeline.
- Passage-level restructuring. Your pages rewritten so individual passages stand alone and answer a question completely without needing surrounding context.
- A corroboration programme. Consistent facts placed in the independent sources these models read, built out over the engagement rather than in one push.
- Entity disambiguation. The naming, description and markup work that stops a model confusing your business with a similarly named one.
- Repeat measurement. The same prompts, run repeatedly, reported with the variance visible rather than hidden.
How the work runs
- Diagnose the mechanism. Ask the category questions and check for citations. Cited means retrieval work. Uncited means the longer corroboration route.
- Fix retrievability. Restructure so passages answer completely and independently.
- Disambiguate the entity. One name, one description, consistent everywhere, marked up so it resolves cleanly.
- Build corroboration. Place consistent facts in the independent sources these models read.
- Measure with variance shown. Repeat prompts, report the spread, never draw a conclusion from a single run.
Proof
The tracking approach, including how variance is handled, is described in the AI visibility audit.
Related
Talk about your situation
The first conversation is short and mostly questions. Get in touch and tell me what you are trying to fix. Or see how this is priced.
Frequently Asked Questions (FAQs)
What is LLM SEO?
Optimising so that large language models name your business when answering questions in your category. It splits into two problems: making your content retrievable and quotable at the moment of asking, and building the corroboration that shapes what a model answers when it is not retrieving anything.
Can you get my brand into ChatGPT's training data?
Not directly, and nobody can. Training data is assembled by the model provider from sources they choose. What is possible is making consistent, accurate information about your business widely available in the kinds of sources these providers draw from. That influences future training runs without controlling them, and it works on a timescale of quarters.
Why does the answer change when I ask the same question twice?
Language models sample from a probability distribution, so identical prompts produce varying output by design. This is why single tests prove nothing and why measurement here uses a fixed question set run repeatedly, with the variance reported rather than smoothed away.
How is LLM SEO different from AEO?
Answer engine optimization is the umbrella term for any system that answers instead of linking, including Google features that predate language models. LLM SEO is the subset concerned specifically with language-model answers and the retrieval and training dynamics behind them. The page-level work overlaps almost entirely.
Does an llms.txt file help?
It is cheap to add and currently does very little on its own, because adoption by the major assistants is limited. Treat it as good housekeeping rather than a lever. The things that measurably move a model answer are retrievable page structure, consistent facts repeated across independent sources, and an unambiguous entity.