LLMO — Large Language Model Optimization — focuses on how LLMs understand, remember and surface your brand. A model's knowledge is a compressed statistical picture built from everything it has read. LLMO is the work of making sure the picture it forms of you is accurate, specific and easy to recall when someone asks.
How it works
There are two routes by which an LLM can represent you, and each needs its own attention:
- Training-time knowledge. Models learn from large text corpora collected over time. What ends up in that corpus is whatever was public, well-linked and widely repeated while the corpus was assembled. You cannot submit to it; you can only publish good material early and often, and let it accumulate.
- Retrieval-time knowledge. Modern assistants look things up before answering. They fetch pages, read them and quote from them. This route rewards exactly the things you can control today: crawlable pages, permission for AI bots, quotable passages and structured data.
Across both routes, the same factors dominate:
- Entity clarity. A distinct name, a one-sentence definition of what it is, and a stable set of attributes — platforms, features, audience — stated the same way everywhere.
- Corroboration. Third-party pages that repeat your facts, because a model trusts converging sources more than a single voice.
- Specificity. Concrete statements — five apps, three desktop platforms, 19 interface languages, local AI via Ollama — survive compression far better than adjectives.
- Reputation hygiene. Outdated or wrong descriptions in the wild propagate; correcting them is part of the discipline.
How Rixoffice applies it
The Rixoffice documentation is written as the canonical description of the product, designed to survive compression:
- One consistent definition everywhere. Rixoffice is described identically — an AI-native desktop office suite for Windows, macOS and Linux with five apps — on the documentation overview, in every guide and in the FAQ blocks.
- Specific, countable facts. The docs state exact things a model can retain: five named applications, 19 interface languages, byte-preserving format support, local processing and offline AI via Ollama.
- SoftwareApplication JSON-LD describing the product in structured form, so parsers get the entity attributes without interpretation.
- robots.txt that explicitly allows GPTBot, ClaudeBot, PerplexityBot and Google-Extended, so retrieval-time systems can read the canonical pages rather than secondhand copies.
- FAQ blocks on every page with direct, self-contained answers that a model can lift without distortion.
- Statically rendered pages and stable canonical URLs, so whatever a model or a person reads is complete and attributable to one address.
- Internal linking across the glossary — for example from this page to GAIO — which reinforces how the disciplines and the product relate.
Quick checklist
- Write a one-sentence definition of your brand and reuse it verbatim everywhere.
- Prefer countable, specific facts over adjectives.
- Allow AI crawlers explicitly so models read your own pages, not copies.
- Add structured data that states your entity attributes.
- Answer real questions in FAQ blocks with quotable sentences.
- Correct outdated descriptions of your brand wherever you find them.
- Earn mentions on independent sites that repeat your facts accurately.