SERVICES
LLMO
Ranking puts you in a list. LLMO decides whether a model names you at all.
Short answer
LLMO (large language model optimization) is the practice of shaping what a language model knows and repeats about a company. It works on entity resolution, on content structured so passages can be extracted intact, and on corroboration across independent sources — because a model answers from what it holds about named organisations, not from a ranked list.
How it actually works
A language model does not search your site when someone asks it a question. It answers from what it already holds about your category, and then, sometimes, it goes and checks. Whether it names you depends on what it holds, not on where you rank.
That is the part of AI visibility most companies never touch, because it lives outside the website. Your entity has to resolve cleanly: one organisation, one address, one description, repeated on sources you do not control. Your pages have to survive being extracted a paragraph at a time. And enough independent places have to agree about who you are that the model treats it as settled.
We work on all three. The first two are weeks of engineering inside your control. The third is quarters of work on directories, review platforms and coverage, and it is the one that finally moves the number.
Three months of this work moved a Greek B2B firm from 0% to 65% mention rate in ChatGPT on a fixed set of 172 buyer questions.
What the engagement covers
- Entity resolution
- One organisation node, one named person, one consistent name and address, declared in structured data and echoed on independent profiles.
- Extractable structure
- Self-contained answer blocks, question-form headings, comparison tables and one concrete fact per section.
- Corroboration
- Directories, review platforms, communities and coverage. The slowest lever and the one that actually decides the outcome.
- Monthly measurement
- A fixed prompt set re-run across five answer surfaces, with verbatim answers archived and dated.
How it runs
- Baseline: what the models say about you today, recorded before anything changes.
- Technical: server-rendered HTML, crawler access, structured data graph.
- Content: rewrite the commercial pages so passages survive extraction.
- Corroboration: build the external profiles the models already trust.
Is it for you?
It is, when
- Your buyers research on ChatGPT or Perplexity before they call anyone.
- You rank well in Google and still never appear in AI answers.
- Your company is described differently on your homepage, LinkedIn and third-party listings.
It is not, when
- Your site is not yet indexed or has serious technical faults. Fix those first.
- You need results in a month. Entity work moves in quarters.
Frequently asked
Is LLMO different from AEO?
They overlap almost completely. AEO describes the objective — being named inside a written answer. LLMO describes the object being worked on — what the model holds about your entity. The work is the same sequence in both cases.
How long before it moves?
The technical and entity layer is weeks and is inside your control. Mention rate moves over quarters, because it depends on sources you do not own. Anyone offering a date is guessing.
Can you guarantee ChatGPT will recommend us?
No, and nobody can. What we can do is measure where you stand, remove the reasons a model cannot name you, and show the change month over month.
Contact us
Send us the brief or book a short call. You get concrete next steps in reply.