Geeks Digital Large language models hold an opinion about your brand. It was formed from everything written about you across the web, it is frozen into their weights between training runs, and it is repeated to every user who asks. LLM Optimization is the practice of shaping that opinion deliberately: correcting what is wrong, strengthening what is right, and making your expertise legible to machines.
What legible means
A model is confident about you when it can state who you are, what you do, who you serve and why you are credible, without inventing anything. That confidence comes from three inputs: a canonical fact source on your own site, structured data that hands over facts in machine-native format, and a corroborating record across independent sources telling the same story. Break the consistency anywhere and the model hedges, blends you with a similarly named company, or leaves you out of answers where you belong.
What we handle
- Model interrogation baseline: what each major LLM currently believes about you, where descriptions are stale and where confusion with other entities exists
- Canonical fact layer: a reference-grade About architecture stating the basics so plainly a machine cannot extract them wrong
- Organization, Person, Product and Service schema, interlinked, with sameAs pointing at your verified profiles
- Disambiguation work for shared or generic names, so a stranger’s reputation stops averaging into yours
- Fact-correction campaigns at the sources models learned the errors from
- Topical association: deep interlinked coverage that teaches models your entity and your subject belong together
Our approach
Interrogate, fix, corroborate, re-interrogate. We keep a written record of every model’s answers at baseline and re-test on a schedule, so improvement is documented rather than vibes. Because training refreshes arrive on the labs’ timetable, we prioritize the retrieval-facing layer first: it moves within weeks and carries most of the same groundwork.
The model says something false about us. Can that be fixed?
Usually, yes, and the path is unglamorous: find the sources the falsehood came from, correct or outweigh them, strengthen the canonical layer, and let retrieval and the next refresh do their work. We have unwound wrong pricing models, dead product lines and founder mix-ups this way.
Is this the same as GEO?
The interrogation protocol
Baselines are only useful if they are repeatable, so ours is a protocol: a fixed battery of identity, offering, pricing, comparison and credibility questions, asked to each major model in fresh sessions, multiple samples per question, answers archived verbatim. The battery reruns after each fix wave and each known model refresh. Over a few cycles you get something almost no brand has: a documented history of what the machines believe about you, and proof of which interventions changed it.
Which mistakes do models make most often about companies?
Stale facts top the list: old pricing models, retired products, former executives presented as current. Then conflation with similarly named entities, then confident summaries of what the company does that miss its actual positioning. All three trace to weak canonical layers and contradictory external records, which is exactly the order we fix them in.
It is the memory layer of it. GEO covers the full answer pipeline; LLM Optimization goes deep on what models believe. Pair it with AI Citation Building for the external record and Entity SEO for the knowledge-graph layer.
