// Concept
What is Generative Engine Optimization (GEO)?
GEO is the practice of making your brand citable inside generative answer engines — ChatGPT, Perplexity, Google AI Overviews, Bing Copilot, Claude, Gemini — rather than just ranking on Google’s blue-link results page.
// Why it exists
Why GEO exists.
Roughly 60% of Google searches now end without a click. AI Overviews answer the query directly; ChatGPT and Perplexity answer it before the user ever opens a tab. The traditional SEO funnel — rank, click, convert — has been quietly amputated. What survives is the citation: the moment an engine names your brand inside its answer. GEO is the discipline of earning that mention reliably, across every engine, across every relevant prompt.
// Difference
How GEO differs from SEO.
SEO and GEO share infrastructure but optimize for different outcomes. The shift looks like this:
| Dimension | SEO | GEO |
|---|---|---|
| Goal | Rank position | Citation share |
| Unit of measurement | Keyword rank | Prompt-level mentions |
| Content lever | Keyword density & topical depth | Entity clarity & citable claims |
| Technical lever | Crawlability, Core Web Vitals | Schema, JSON-LD, llms.txt |
| Reporting cadence | Weekly / monthly | Hourly per engine |
| Primary competitors | Top 10 SERP | Whoever the engine names |
// Shape
What good GEO looks like.
- Schema markup, everywhere it matters. Product, FAQ, HowTo, Organization, Article, DefinedTerm. Engines reward sites that hand them structure; they punish sites that hide it.
- An llms.txt and llms-full.txt. A canonical map of the content you want ingested, in the format ingestion pipelines actually prefer.
- Entity completeness. Your brand, products, founders, and categories defined consistently across your site, Wikipedia, Wikidata, Crunchbase, and LinkedIn so the engine’s entity graph resolves cleanly.
- A monitored citation graph. You know which engines cite you, for which prompts, against which competitors — and you watch the deltas weekly.
// Pitfalls
Common GEO mistakes.
- Treating it like SEO with new keywords. Stuffing prompts into H1s doesn’t move citation share. Engines extract entities and claims, not phrases.
- Optimizing for one engine. The brand that wins ChatGPT often loses Perplexity. A real program tracks all six.
- Ignoring the off-site graph. Engines read your Wikipedia, your G2 page, your founder’s LinkedIn. If those disagree with your site, the engine picks the wrong one.
// Vizelo
How Vizelo operationalizes GEO.
Vizelo measures your brand’s share of voice across every major answer engine, finds the prompts where your competitors are winning, ships the schema and content changes that move citations, and re-measures hourly. It is GEO as a working system rather than a quarterly slide deck. Start free to see your current footprint in under a minute.
// Related
Keep reading.
FAQ
Common questions.
How is GEO different from SEO?
SEO optimizes for rank position on a results page. GEO optimizes for citation inside an AI-generated answer. SEO measures clicks and impressions; GEO measures whether your brand is named, linked, or summarized when the engine answers a buying-intent prompt.
Do I still need to do SEO?
Yes. AI engines crawl, rank, and re-summarize the same web. Strong technical SEO — fast pages, clean schema, internal linking — is a prerequisite for GEO, not a replacement for it. GEO is what you do on top once the SEO basics are healthy.
Which engines does GEO target?
The six that matter today: ChatGPT, Perplexity, Google AI Overviews, Bing Copilot, Claude, and Google Gemini. Each weighs sources differently. A GEO program tracks citation share across all six and surfaces where you’re winning and losing.
Can you do GEO without changing your website?
Not really. You can monitor without changing anything, but lifting citations almost always requires schema additions, content rewrites, entity-clarification work, and an llms.txt. The site is where the change lands.
How long does GEO take to show results?
First citation lifts usually appear within 2–6 weeks of shipping schema and llms.txt work. Compounding gains — moving from sporadic to consistent recommendations across multiple prompts — take a quarter. AI engines reindex at different cadences, so changes show up unevenly.