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LLMO

Large Language Model Optimization: Control What AI Says About You

Your buyers are asking AI tools whether you are any good. The answer is being assembled from sources you have never checked.

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How we operate

  • No % of ad spend. Ever.

  • No contracts. Cancel anytime.

  • Guaranteed geo & market exclusivity.

What this is

Ask an AI about your company and see what comes back

Do it before you read any further. Ask one of the major AI tools what your business does, where it operates, and whether it is any good. Most owners find at least one thing that is wrong: a service you dropped years ago, an address you moved from, a competitor’s work credited to you.

That answer is being given to your buyers. Nobody sees it happen, there is no analytics entry for it, and there is no complaints inbox for a wrong summary.

Large language model optimization is the work of correcting it. Not by editing a model, which nobody can do, but by fixing the sources it reads until the accurate version of your business is the one the web repeats most.

LLMO vs GEO

Being Named and Being Described Correctly

Two different AI problems. Being on the shortlist with a wrong description is its own kind of expensive.

Swipe to compare

Being Named and Being Described Correctly
TopicGEOLLMO
The question it answersWhen someone asks about your CATEGORY, does the answer name you?When someone asks about YOUR BUSINESS, is the answer accurate?
What it targetsThe sources retrieved and cited for a given questionThe model’s default understanding of you as an entity
Main leverQuotable evidence plus topical corroborationConsistent facts about you, repeated across independent sources
Failure modeThe answer names competitors and not youThe model states something wrong, stale, or merged with another firm
How fast it movesFaster, because retrieval reflects the live webSlowest of everything we sell. Some of it waits on model updates
Why it mattersYou are missing from the shortlistYou are on the shortlist, described wrongly

What we do

Define, Spread, Correct

Agree what is true, make the web repeat it, then watch what the models actually say.

Define the entity

A model can only describe your business accurately if the web agrees on what your business is.

One canonical fact set

Legal name, trading name, services, service area, founding date, credentials. Written down once, then made identical everywhere you appear.

Machine-readable identity

Organization and LocalBusiness markup, consistent naming, and linked profiles so the business is parsed as one entity rather than several similar ones.

Disambiguation

If another company shares your name, or you have rebranded, the model needs help telling the versions apart. Otherwise it merges them.

Spread the facts

Models learn what the web repeats. One authoritative page is not repetition.

Third-party consistency

Directories, review platforms, professional listings, and press carrying the same facts. Contradictions across sources produce hedged or wrong summaries.

Being written about

Coverage, interviews, and independent mentions. A business the web discusses is one a model can describe with confidence.

The review corpus

What customers say publicly becomes part of how you are characterised, not just how you are rated. Volume, recency, and specifics all count.

Watch and correct

The only part of this you can act on quickly, and the part almost nobody does.

Output monitoring

Asking the major models about your business on a schedule and recording what they say, so drift and errors are caught rather than discovered by a customer.

Error triage

When a model says something wrong, we trace it to the sources feeding the mistake. Usually it is a stale directory listing, not the model inventing things.

Source correction

Fixing the underlying sources, since that is the only durable lever. You cannot edit a model, but you can change what it reads.

How we work

How an LLMO Engagement Runs

  1. Baseline

    Ask the models about you

    We query the major systems about your business by name and record the answers verbatim, several times each, because the answers vary.

    We are looking for:

    • Whether the model knows you exist at all
    • Services or specialities it attributes to you that you do not offer
    • Wrong location, wrong service area, or a merged identity with another company
    • Stale facts: old pricing, closed locations, previous branding, former staff
    • How it characterises you against named competitors

    For most businesses the first run produces at least one thing that is simply wrong. That is the fastest value in this service.

  2. Define

    Agree the canonical facts

    We write down the definitive version: what the business is called, what it does, where it operates, what it does not do, and the claims it can support.

    This sounds trivial and rarely is. Most businesses discover their own sources disagree, which is exactly why the summaries are wrong.

  3. Align

    Make the web agree

    Site markup, profiles, directories, and listings are corrected to match the canonical set, starting with the sources that actually get read.

    Stale listings on forgotten platforms are a common culprit. A directory entry nobody has looked at since 2019 is still being read by systems that do not know it is abandoned.

  4. Reinforce

    Build corroboration

    Then the slower work: being independently written about, reviewed, and referenced, so the correct version of your business is the one repeated most often.

    This is where LLMO and demand generation overlap. Being genuinely well known is the most durable way to be accurately described.

  5. Monitor

    Re-test on a schedule

    The same prompts, run on a schedule, with changes tracked. Models update, sources change, and errors reappear.

    We report what improved, what regressed, and what stayed wrong despite the work, because some of it will.

The Limits of This Work

This is the least controllable service we sell. It is worth buying for specific reasons and not others, and the difference matters enough to spell out before you spend anything.

You cannot edit a model

There is no dashboard, no submission form, and no support ticket that changes what a model believes about your company. Anyone offering direct correction is describing something that does not exist.

What exists is indirect: change the sources, wait, and re-test. Answers drawn from live retrieval improve relatively quickly. Anything baked into trained knowledge may not shift until a future model version, on a timetable nobody outside those companies controls.

Most errors are your own record, not the model

The usual finding is dull and fixable. A directory entry with an old address. A profile listing a service you stopped offering. A previous business name still attached to half your listings. A similarly named firm two towns over getting merged with you.

Models are reflecting a contradictory record rather than hallucinating from nothing. That is good news: contradictions are correctable, and the fix also improves your local search presence, which is a real return whatever happens to AI adoption.

Some things will stay wrong

Occasionally a claim persists no matter how thoroughly the sources are cleaned, because it is embedded in a model generation. It will likely disappear at the next update. It might not.

We would rather name that limit than keep invoicing against an outcome we cannot deliver. If we reach the end of what sources can fix, we will say so and stop.

Accuracy is the case, not visibility

If you are hoping this fills your pipeline next quarter, it will not. The argument for LLMO is defensive: wrong information about your business is already costing you enquiries you never hear about, and it compounds quietly because nobody complains about a summary.

For most businesses the correct sequence is lead generation first, then SEO and AEO, then this. We will tell you that if it applies, and it usually does.

Where it is genuinely urgent

Businesses that have rebranded, moved, merged, or changed what they sell. Businesses sharing a name with another company. Regulated fields where a wrong description carries a compliance risk rather than just a lost lead. Any business where an AI tool is already stating something false.

If one of those is you, this stops being an emerging-channel bet and becomes ordinary reputation maintenance.

Why us

What Makes This Different

  • We will show you the raw answers

    You get the model outputs, verbatim, including the unflattering ones. This service is too easy to fake with a summary nobody can check.

  • We fix sources, not prompts

    You cannot edit a model. The durable lever is correcting what it reads, which is slower and the only thing that actually holds.

  • Honest about the ceiling

    Some errors will persist through a model generation whatever anyone does. We will tell you which, rather than billing indefinitely against them.

  • Nothing manipulative

    No fake reviews, no seeded content designed to mislead a model. Beyond the ethics, planted claims get contradicted by real sources and make summaries worse.

  • One client per market

    When a model is asked to compare businesses in your category, we are not shaping the answer on behalf of two of you.

  • Flat fees

    No percentage of ad spend, and no reason to inflate an emerging service beyond what it can honestly deliver.

Questions

Large Language Model Questions

LLMO is the work of shaping what AI models say about your business when someone asks about you by name. It targets the model’s underlying understanding of you as a business, rather than any single search result.

Related

Where to Go Next

Find Out What the Models Are Saying About You

Book a strategy call. We will run your business through the major AI tools and show you the raw answers, including anything that is wrong.

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