Stop chasing one AI engine
at a time. Optimize the model.

LLM SEO measures whether a business appears when buyers ask category-relevant questions across systems such as ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews, then strengthens the controllable public evidence those systems may retrieve and cite. The work can include search visibility, entity clarity, citation-ready content, and independent corroboration. Outputs vary by engine, prompt, and date, so no single action guarantees coverage across every model.

Per-engine work accounts for differences in source selection, citation format, and retrieval behavior. LLM SEO starts with the shared controllable foundations: crawlable pages, entity clarity, attributable content, and independent corroboration. Those foundations can support several assistants, while each engine still needs its own prompt baseline and observation. We do not claim that one universal optimization controls every current or future model.

They overlap heavily and the terms are used interchangeably in the market. Generative engine optimization (GEO) and answer engine optimization (AEO) both describe getting cited inside AI-generated answers. 'LLM SEO' and 'LLM optimization' name the same discipline from the model's side: the large language model is the thing you are optimizing for. We treat GEO as the category pillar and LLM SEO as the engine-agnostic layer within it: the work that applies to language models as a class before you tune for any single assistant.

The exact process varies by engine, prompt, and date. Useful observable layers include live retrieval, source selection, extraction, and entity disambiguation. A model may use its existing knowledge as well, but an operator cannot directly edit or verify the model weights. We focus on controllable inputs such as crawlability, search visibility, attributable content, schema, sameAs signals, and independent corroboration, then record which sources the supported engines actually cite.

Both, and the split depends on the engine. Perplexity and Google AI Overviews cite sources with clickable links and send measurable referral traffic. ChatGPT and Claude more often answer by name without forwarding the click, but a buyer who sees your business recommended inside the answer has been pre-sold before they reach your site, which shows up as branded-search lift and stronger direct traffic. We measure both the direct citations and the downstream demand, because LLM mentions create intent that resolves through other channels.

We track it directly with Outrigger, the independent AI-visibility platform Joel co-founded with Andrew for exactly this. For each priority keyword we log whether your brand was mentioned, whether it was cited with a link, the context and sentiment of the mention, and which page was the source: across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews, weekly. Then we benchmark that against your top three competitors on the same queries. The composite is a citation-share metric: your slice of the AI answers in your category, tracked over time. Most agencies selling LLM optimization have no instrumentation and are guessing at whether it works.

A model can draw on existing knowledge as well as live retrieval, but an operator cannot force a new mention into model weights or verify when a provider will update them. That is why Xpand treats public authority as corroboration, not a model-training promise. When digital PR is in scope, Xpand may use PressForge to organize research, pitching, and follow-up. Any coverage remains subject to editorial judgment, and each engine decides whether to retrieve or cite it.

The retrieval layer and the training layer move on different clocks, and neither supports a universal outcome date. Indexed content and structure can be reviewed as implementation milestones, while citation movement depends on the starting authority, query set, source quality and each model's retrieval or update behavior. We establish the baseline first, then set an evidence-dependent review cadence for leading and commercial indicators.