AI SEO vs LLM SEO vs GEO: what should companies build first?

AI SEO, LLM SEO, ChatGPT SEO, AI search optimization, and GEO are overlapping names for the same buyer problem: the company is not being named, described, or cited when prospects ask AI systems for recommendations.

The useful answer is not to pick a label and write thin pages around it. The first useful build is a source-material layer that explains the offer clearly, answers the exact buyer questions, connects to commercial pages, and gives AI systems consistent facts to reuse.

For TWOHUNDRED, the public label is GEO, or Generative Engine Optimization. The support content should still include AI SEO and LLM SEO language because buyers use those terms when they search and when they ask AI systems for help.

Traditional SEO is still the foundation. If Google cannot crawl a page, if internal links are weak, or if the page does not answer the query, AI search visibility will not have a stable source to build from.

AI SEO usually describes visibility inside AI-assisted search results and AI summaries. It cares about headings, snippets, entity clarity, schema, citations, and whether the page gives a clean answer early enough to be extracted.

LLM SEO is more concerned with how language models understand the brand and category. It rewards consistent wording, corroboration across sources, public entity profiles, and repeated associations between the company and the problem it solves.

GEO is the operating system that joins those pieces. It tracks prompts, source URLs, citation gaps, competitor mentions, and the page or distribution change required to move the answer.

Start with the commercial page that should be cited when buyers ask for help. That page needs a direct explanation, proof-safe service language, schema, related links, and a reason for the AI system to connect the company with the category.

Next, build one support article that answers the messy comparison question. For this cluster, that question is AI SEO versus LLM SEO versus GEO. The page should define the terms, explain when each matters, and point readers back to the canonical GEO service page.

Then expose the pages in discovery assets: sitemap, llms.txt, llms-full.txt, blog index, and internal links. A page that exists but is hard to discover will not become a reliable source for AI answers.

Prompt measurement should use buyer-like questions, not vanity prompts. Ask what AI SEO is, who helps companies appear in ChatGPT, how to choose an LLM SEO agency, and which companies offer generative engine optimization for business visibility.

For each engine, record whether the brand was mentioned, where it appeared, which URL was cited, which competitors were named, what the answer said, and when the test ran. That turns GEO from a content guess into a measurement loop.

If TWOHUNDRED is absent, the next action is not another generic article. It is a specific fix: clearer answer section, stronger internal links, schema improvement, distribution packet, authority URL capture, or a new support page for the missing question.