How LLMO works
LLMO stands for large language model optimisation. Where answer engine work targets a live lookup — the model searches, finds your page, quotes it — LLMO targets the other source a model answers from: what it already holds about you from training, and what everyone except you has written about your business.
That distinction sets the whole approach. You cannot edit a model’s weights, file a correction or push an update. What you can change is the material the next model reads: your own site, but far more importantly the places where other people describe you — directories, publications, forums, supplier and partner pages, reviews, interviews. Consistency across those is the lever, and repetition over time is the mechanism.
Why LLMO matters
Because a model asked to recommend someone in your category will name whoever it can describe with confidence. If the web says little about you, or says contradictory things, you are a risky name for it to offer, and the enquiry goes to a business it knows better. That is a quiet loss: nothing in your reports records the times you were not mentioned.
It matters more for businesses selling across borders. A consultancy in Kathmandu working with clients in Australia or the UK is judged by a model on what the wider web can confirm, not on a local reputation that was never written down anywhere. Getting that reputation into public writing is most of the work.
Where LLMO goes wrong
The first mistake is expecting speed. Anything resting on training data moves on the schedule of the next model, not yours, so results are slow, uneven between systems and impossible to attribute cleanly. Anyone promising a quick change in what a model says about you is describing something they do not control.
The second is trying to game recall with volume: mass-published pages, spun descriptions, paid mentions on sites nobody reads. That output is interchangeable, and interchangeable text is precisely what a model has no reason to remember. The third is neglecting the corrections that are genuinely available — an out-of-date profile, a wrong address, an old service list still sitting on a directory nobody has looked at in years.
What to do about it
Begin with a record. Ask several models the questions a prospect would ask, in the markets you sell to, and write down exactly what each one says about you, including what it gets wrong and who it names instead. Without that baseline you will never know later whether anything moved.
Then fix sources rather than symptoms. Update every profile and listing so the facts agree, get accurate descriptions of your work published where humans genuinely read them, and give the web something specific to repeat — a method, a constraint, a distinct focus — instead of adjectives. The faster-moving, page-level side of this is answer engine optimisation, and the combined discipline is what the site groups under AI search optimisation.