Generative Engine Optimization — be the source ChatGPT, Perplexity and Claude cite
Search behaviour is bifurcating. Gartner forecasts a 25% drop in traditional search volume by 2027. The replacement is generative answers — and the brands cited inside those answers are the ones that win the next decade. Generative Engine Optimization (GEO) is the discipline of becoming the source LLMs trust. It is not "SEO with an AI flavor" — it is a different measurement model, a different content shape, and a different schema posture. We build the full setup.
Why GEO is its own discipline
Traditional SEO optimizes for ranking. GEO optimizes for citation. The signals diverge in three places.
-
Citation, not click
LLMs do not rank ten blue links — they synthesize. The win condition is being one of 3–7 sources cited in the answer. That requires distinct, dense, citable claims, not headline keywords.
-
Entity over keyword
LLMs reason over entities (people, places, organizations, products) and their relationships. A schema graph that declares your entity edges (sameAs, knowsAbout, areaServed, makesOffer) is more valuable than a keyword cluster.
-
Source-fitness, not authority alone
Domain authority matters less than source-fitness: "is this URL a tight, citable answer to this exact query?" Pages built for SERP CTR (clickbait titles, scroll-bait copy) score worse in LLM retrieval than pages built for citation density.
The GEO stack we deploy
Five components. We build them in order, and the citation curve compounds across all three of ChatGPT, Perplexity and Google AI Overviews.
-
Schema graph
Organization → LocalBusiness → Service → Place → Person → Article. Cross-referenced via @id, sameAs and mentions, so the entity graph is fully resolvable. Validated against Google's Rich Results test and Schema.org SHACL.
-
llms.txt + AI-friendly markup
A root /llms.txt manifest pointing crawlers at canonical citation pages. Per-page <meta name="ai-content-declaration"> and ai-readability hints. ARIA-clean HTML so retrieval rankers parse cleanly.
-
Citability copy
A specific writing pattern: definition → claim → evidence → source. Each section is a self-contained citable unit. Numbers, dates and proper nouns are explicit (not "recent surveys" — actual citations with year).
-
Entity reinforcement
Brand SERP and Wikidata cleanup, knowledge panel claim verification, sameAs links to authoritative profiles, internal entity pages for founders/products/methodologies.
-
Citation measurement
Weekly probes of your top 100 queries across ChatGPT, Perplexity and Google AIO. Citation share dashboard. Gap-to-fix loop into the content engine.
Build sequence
Four to six weeks from kickoff to a measurable lift in citation share.
-
Week 1
Audit + entity model. Citation baseline across 100 queries × 3 LLMs. Entity graph designed. Schema gaps catalogued.
-
Week 2
Schema deployment. Organization + LocalBusiness + Service + Article schema rolled out site-wide with cross-references. llms.txt published.
-
Week 3–4
Citability copy. Top 30 cited pages rewritten in citation-density format. New entity pages published for unresolved knowledge edges.
-
Week 5
Reinforcement. sameAs cleanup, Wikidata edits, knowledge panel claims, brand SERP audit.
-
Week 6
Measurement. Citation dashboard live, weekly probes scheduled, gap-to-fix loop handed over.
What clients ask about GEO Citability Setup
No — it sits alongside SEO. Traditional ranking still drives clicks for transactional queries. GEO drives presence in informational, comparison and question-format queries, which is where AI answers concentrate. Brands that win the next 5 years run both.
No. The schema graph and llms.txt are platform-agnostic. We deploy on WordPress, Shopify, custom MVC, Webflow and Next.js. The renderer is the implementation detail; the entity model is the strategy.
We probe each of your tracked queries weekly via the Perplexity API, OpenAI search-grounded calls, and a headless Google AIO crawler. The dashboard shows citation share per LLM per query, week-over-week, with gap-to-fix prompts.
Important distinction: blocking GPTBot or Google-Extended affects training, not retrieval. Search-grounded LLMs (Perplexity, ChatGPT search, Google AIO) fetch via search-engine crawlers — which respect Googlebot/Bingbot, not GPTBot. We help clients calibrate the policy without blocking citation paths.
No serious operator can — LLM ranking is non-deterministic. We can guarantee the schema is valid, the citability copy is in place, the entity edges are declared, and the measurement loop is running. In our portfolio, that combination has lifted citation share on tracked queries by 2–4× within 90 days.
Pairs well with
Ready to scope GEO Citability Setup?
Tell us your goal. One reply, one human, within 24 hours.
Book the GEO setup call →