AI Search Optimization (GEO) for Premium Service Brands.

AI search optimization (often called GEO, generative engine optimization) is the practice of making a brand visible and accurately described in AI-driven search: ChatGPT, Perplexity, Claude, Gemini, and Google's AI Overviews. Where classic SEO earns a position in a list of links, GEO earns retrieval and citation inside a composed answer. The work is concrete: structuring pages so an AI can extract a correct answer from them, publishing machine-readable knowledge files like llms.txt, maintaining entity-level schema so the systems know exactly who you are, and admitting AI crawlers rather than blocking them.

They share a foundation — clean technical structure, real content, honest claims — and diverge from there. Traditional SEO optimizes for ranking signals: links, keywords, position on a results page. AI search optimization optimizes for retrievability and quotability: whether a system reading your site can extract a correct, self-contained answer, and whether it can resolve your firm as an unambiguous entity. A page can rank first on Google and still never be cited by an assistant because its answers are buried in marketing prose. We build for both, and most of the GEO work (schema, structure, llms.txt) reinforces classic SEO rather than competing with it.

No, and nobody honestly can. Whether ChatGPT or Perplexity cites a given brand in a given answer is decided by the model and its retrieval layer, not by any vendor, and a guarantee of placement in a system nobody controls is a red flag. What is controllable: whether AI crawlers can read your site, whether your pages are structured so answers can be extracted from them, whether your entity data is unambiguous, and whether machine-readable surfaces like llms.txt exist. We do all of that, measure AI-referred visits so you can see what changes, and skip the guarantee theater.

Three surfaces, each reported with its limits stated. First, AI-referral attribution: visits arriving from chatgpt.com, perplexity.ai, claude.ai, gemini.google.com, and copilot.microsoft.com are classified at first touch and followed through to booking. Second, AI crawler activity against the pages we ship. Third, periodic spot-checks of what the major assistants actually say about the brand. The honest caveat: AI assistants often strip or omit referrers, so measured AI traffic undercounts real AI influence — treat it as a floor, not a total.

Verifiably. This site runs the full playbook: llms.txt and llms-full.txt at the root, a public read-only MCP server documented at /connect, an annotated research hub at /research with primary sources linked and caveats intact, FAQ and entity schema on every major surface, and AI-referral classification in our own attribution layer. Nothing on this page is proposed that is not already in production here.