DIVISION 04 · AI VISIBILITY
LLMO
Large Language Model Optimization - the technical and content work that makes a brand's knowledge easy for LLMs to ingest, ground and retrieve.
- Who it's for
- Businesses with deep product or domain knowledge and a technical bent.
- How it's measured
- Retrieval accuracy and grounding quality on your knowledge domain.
- Typical timeline
- Foundational work up front; retrieval quality improves throughout.
THE APPROACH
What it is
LLMO is the lower-level counterpart to AEO and GEO: making your knowledge machine-ingestible. It covers the formats, feeds and structures that let a model - or a retrieval system built on one - find and ground itself in your material.
Where it applies
Two places: the public engines that crawl the open web, and any retrieval-augmented system your own product or partners run. The underlying discipline is the same - clean, chunkable, well-labeled knowledge.
How we approach it
We structure your knowledge into retrievable units, add the metadata and relationships that aid grounding, and remove the ambiguity that causes models to hallucinate or misattribute.
What we change
Content chunking, canonical facts, entity relationships, machine-readable feeds, and the internal linking that expresses how your knowledge fits together.
How we measure it
Retrieval accuracy on a benchmark of domain questions, and grounding quality - whether answers cite the right unit of your content.
What you get
A model-ready knowledge layer, documentation of how it's structured, and a benchmark you can re-run as your content grows.
QUESTIONS
LLMO, in plain answers
Is LLMO only relevant if we build our own AI product?
No. The same structuring that helps your own retrieval system also helps public engines ingest and ground themselves in your content.
Do you work with our engineering team?
Yes - LLMO is often a joint effort with your engineers, especially where feeds and retrieval systems are involved.
THE OTHER DISCIPLINES
LLMO rarely runs alone. The rest of AI Visibility:
AI SEO
AI Search Engine Optimization
Optimizing a brand's content and structure so it is surfaced, understood and cited by AI search engines - not just ranked by classic ones.
AEO
Answer Engine Optimization
Answer Engine Optimization - structuring content so answer engines can extract, trust and quote it directly in a generated response.
GEO
Generative Engine Optimization
Generative Engine Optimization - improving how generative models represent your brand across the answers they produce, at scale.
SEO
Search Engine Optimization
Classic search engine optimization - still the foundation, because the pages that rank are often the sources AI engines cite.
SMAO
Social Media Answer Optimization
Social Media Answer Optimization - shaping the social and community signals that models increasingly draw on when they answer.
AIO
AI Optimization
AI Optimization - the umbrella discipline covering every surface where an AI system forms an impression of a brand, from the owned site to the third-party corpus models are trained and grounded on.