GEO, Generative Engine Optimisation

GEO (Generative Engine Optimisation) is the practice of preparing a website so that generative AI systems, ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews, and Bing Copilot, can surface, summarise, and cite its content. It is the umbrella term for the work most marketers know as AEO (Answer Engine Optimisation) and LLMO (Large Language Model Optimisation).

Why GEO matters now

The way people find information is changing faster than at any point since the launch of mobile search. Google AI Overviews now appear on a meaningful share of informational queries, and click-through rates to underlying sources drop sharply on those queries. ChatGPT, Perplexity, and Claude collectively send a small but rapidly growing slice of referral traffic, and, more importantly, they shape the answers users act on before they ever click a link.

Sites that are easy for generative engines to read, extract, and trust are the ones that get cited. Sites that are not optimised for these systems become invisible in the answer layer, even when they still rank in classic blue-link search.

GEO vs AEO vs LLMO

The three acronyms describe overlapping work. CiteFlow treats GEO as the parent discipline and AEO and LLMO as its two pillars:

If you fix AEO and LLMO issues, you are doing GEO. There is no separate GEO checklist, the work lives inside the AEO and LLMO pillars of your audit.

How CiteFlow scores GEO

Your audit already produces two pillar scores that together capture generative-engine readiness:

• AEO score, answer-extractable structure (question headings, FAQ schema, snippet structure, direct-answer paragraphs, internal linking, schema validity).
• LLMO score, citation-worthy signals (author and organisation schema, breadcrumbs, citation structure, content depth, outbound authoritative links, AI crawler allowlist, llms.txt).

CiteFlow synthesises these into a GEO score using an equal 50/50 weighting:

GEO = round((AEO * 0.5) + (LLMO * 0.5))

The GEO score appears on every audit results page underneath the per-pillar tiles. It is a presentation-layer summary, there are no separate GEO findings, and the SEO score is intentionally excluded so the GEO number reflects AI-engine readiness specifically.

How to improve your GEO score

Because GEO is synthesised from AEO and LLMO, anything that improves either pillar improves your GEO score. The highest-leverage actions for most sites:

1. Allow AI crawlers in robots.txt. GPTBot, ClaudeBot, PerplexityBot, and Google-Extended must be able to read your pages.
2. Publish an llms.txt file at your domain root describing your site to large language models.
3. Add FAQPage schema on any page with a question-and-answer pattern.
4. Lead with a direct answer. The first paragraph of every informational page should answer the headline question in 1–2 sentences before expanding.
5. Add Article, Author, and Organisation schema. Generative engines weight identified, accountable content more heavily.
6. Link out to authoritative sources. Citing your own sources makes you more citable in turn.
7. Keep schema consistent across templates. Inconsistent or broken JSON-LD is the single most common reason AI Overviews skip an otherwise strong page.

Each item above maps to one or more existing finding keys in the audits module, work the audit's action plan top-down and your GEO score will follow.

How important is GEO for growth?

For most B2B, SaaS, content, and professional-services sites, GEO is the next 12–24 months of incremental visibility. It does not replace SEO, the same content asset usually serves both, but it does determine whether your work is read by the systems that increasingly sit between users and the open web. Sites that treat GEO as a first-class concern today will compound that advantage as generative engines take a larger share of attention.