Generative Engine Optimization
Be the answer AI gives when your buyers ask the questions that matter — not just a link they might never click.
What you'll learn in this guide
- What GEO means and how it differs from traditional SEO and keyword optimisation
- How AI models decide which brands to cite — and what signals move the needle
- Why entity optimization is now as important as link building for B2B visibility
- A 5-step GEO process from baseline audit to sustained Share of Model growth
- How to track your AI citation rate across ChatGPT, Perplexity, and Google AI Overviews
What is Generative Engine Optimization?
Generative Engine Optimization (GEO) is the practice of structuring your content, authority signals, and entity data so that large language models — ChatGPT, Perplexity, Google Gemini, Microsoft Copilot, Claude — surface your brand as a trusted answer when buyers ask questions relevant to your category.
Traditional SEO was built around a single question: can Google find and rank my page for a given keyword? GEO is built around a different question: when an AI model synthesises an answer about my category, does it cite my brand as an authority? These are not the same question, and the mechanics of answering them well are fundamentally different.
The distinction matters because AI engines don't return a list of links for the user to evaluate. They return an answer — typically one to three paragraphs synthesised from multiple sources, often with a small set of cited references. If your brand is cited in that answer, you've captured a high-quality discovery moment with a buyer who was actively researching. If you're not cited, you've been bypassed entirely, regardless of your traditional search rankings.
How AI models decide what to cite
Understanding how AI engines evaluate sources is the foundation of effective GEO. While the exact mechanisms vary by engine, several principles apply consistently across ChatGPT, Perplexity, and Google AI Overviews:
- Topical depth over breadth — AI models favour sources that cover a topic comprehensively and authoritatively, not sites with thin coverage across many unrelated topics
- Clear entity signals — your brand needs to be unambiguously associated with your category in the data AI models use. This means your website, third-party mentions, and structured data all reinforce the same entity picture
- Content structure — directly stated facts, clear headings, concise definitions, and bulleted lists are significantly more extractable than dense prose paragraphs
- Citation history — brands that are already cited by credible third-party sources are more likely to be cited again. Authority signals compound over time
- Recency and freshness — for retrieval-augmented systems like Perplexity, recently published content is weighted more heavily, making consistent publishing cadence important
The practical implication: GEO is not about tricks or shortcuts. It's about building the kind of genuine topical authority that makes your brand the natural, trustworthy choice when an AI model needs to cite someone in your category. That takes deliberate strategy and sustained execution — which is where we come in.
We'll run prompts across ChatGPT, Perplexity, and Google AI Overviews and show you exactly what AI engines say about your brand — and what your competitors are saying instead.
GEO built on three foundations.
Entity & Brand Signals
AI models build a picture of your brand from hundreds of signals across your website, third-party mentions, review platforms, and structured data. We audit and strengthen every signal — ensuring your brand name, category, products, and key people are consistently and correctly represented across every source AI engines query. A strong entity profile is the foundation that makes every other GEO investment more effective.
Content Architecture for AI
How you structure information matters as much as what you say. We audit your existing content and reformat it for machine readability — clear definitional paragraphs, logical heading hierarchies, structured lists, and explicit Q&A sections that AI models can extract and cite directly. New content is written to these standards from the start, creating a library that is simultaneously useful for human readers and optimally legible for AI engines.
Structured Data Implementation
Schema markup — FAQPage, HowTo, Organization, and Article types — communicates directly to AI systems what your content covers and why it should be trusted. We implement a comprehensive structured data strategy across your site, ensuring AI models can accurately classify your content, understand your entity relationships, and confidently cite you when your topic territory comes up in buyer queries.
The GEO process.
GEO baseline audit
We begin with a systematic audit of your current AI visibility. Running 40–60 prompts across ChatGPT, Perplexity, Google AI Overviews, and Copilot, we map exactly where your brand appears, how it is described, how often competitors are cited instead of you, and which topics in your category you are absent from entirely. This baseline is the honest picture your strategy needs to be built on.
Entity authority mapping
We audit every source AI engines use to understand your brand — your website's About and service pages, LinkedIn company profile, Crunchbase, industry directories, press coverage, review platforms. We identify gaps and inconsistencies, then systematically close them. A brand with strong, consistent entity signals across multiple sources is far more likely to be cited than one with a fragmented or ambiguous digital footprint.
Content restructuring for AI readability
We audit your existing content library against GEO readability criteria: clear H2/H3 structures, definition-first paragraphs, bulleted lists for enumerable points, and standalone Q&A sections. Content that buries key facts in long prose is reformatted for extractability. Where topic gaps exist — questions your buyers ask that you don't currently answer — we produce new content that fills them with the depth AI engines reward.
Schema and structured data implementation
FAQPage schema on content-heavy pages, HowTo schema on process-driven articles, Organization schema on your homepage and About page, Article schema with proper authorship signals — we implement a comprehensive structured data layer that tells AI models precisely what your content covers. This is one of the most direct signals available to influence how AI engines classify and cite your brand.
Monitor Share of Model and iterate
GEO is not a one-time project — it's an ongoing programme. Monthly, we re-run your prompt library across AI engines, tracking your Share of Model against baseline and against competitors. You receive a report covering citation rate changes, which topics you're winning or losing, how your brand description is evolving, and the specific actions we're taking to improve. This feedback loop drives compounding improvement over time.
We work with B2B companies across SaaS, professional services, and tech to build sustained AI search visibility. Tell us about your category and we'll show you the opportunity.
Senior talent. No layers.
GEO-native, not retrofitted
We didn't add GEO to a traditional SEO service list. We built our methodology around how AI engines work — entity signals, content structure, citation patterns — from the beginning. There's a difference between an agency that understands GEO and one that has always worked this way.
Content and technical in one team
Effective GEO requires both strong editorial thinking (content that deserves to be cited) and solid technical execution (structured data, entity architecture). Most agencies are one or the other. We bring both, working as a single team without briefing chains or handoffs between specialisms.
Transparent Share of Model reporting
Your monthly report shows exactly what changed, what we did, and what we're doing next. Share of Model is a real number — not a proxy metric or a slide full of impressions. You see your citation rate move, and you understand why.
Common questions.
GEO is the practice of optimising your digital presence so that generative AI engines — ChatGPT, Perplexity, Google Gemini, Claude — cite your brand when answering relevant queries. It goes beyond traditional keyword SEO to include entity authority, citation-worthy content, structured data, and the kind of topical depth that AI models use to decide which sources to trust and recommend. The goal is to be the answer AI gives, not just a link in the results below it.
Traditional SEO focuses on ranking in Google's blue links using keywords, backlinks, and page authority. GEO targets the AI-generated summaries and recommendations that increasingly sit above — or replace — those links. GEO requires deep topical content, consistent brand entity signals, and an understanding of how large language models evaluate and cite sources. The signals are different, the tools for measuring success are different, and the content strategy required is different — though a strong traditional SEO foundation does provide a useful base to build from.
A comprehensive GEO programme covers ChatGPT (OpenAI), Perplexity, Google AI Overviews (Gemini), Microsoft Copilot (Bing), and Claude (Anthropic). Each engine has slightly different retrieval and citation patterns — Perplexity performs live web retrieval and cites sources explicitly, while ChatGPT draws on training data plus browsing. The core principles — topical authority, entity clarity, structured content, and credible third-party citations — apply across all of them and form the backbone of every programme we build.
AI engines consistently favour content that directly answers questions in a structured, extractable way. Definition-first paragraphs (starting with "X is..."), clear heading hierarchies, numbered and bulleted lists for multi-part answers, and explicit FAQ sections all perform significantly better than dense prose. Pages that answer one question comprehensively outperform pages that touch many topics lightly. Content should be written as if answering a specific question a buyer might ask an AI assistant — because that is exactly how it will be used.
The primary metric is Share of Model — your citation rate across AI engines on a defined set of category-relevant prompts, tracked month over month. We also measure citation sentiment (how your brand is characterised), topic coverage (the breadth of queries where you appear), and directional signals like branded search volume and direct traffic that typically rise alongside improved AI visibility. Tools we use include Profound for AI search monitoring and Ahrefs Brand Radar for tracking cross-platform brand mentions.
GEO and AI SEO are closely related and often used interchangeably. GEO tends to emphasise the content structure and entity optimisation signals specific to large language models, while AI SEO is the broader service category that includes GEO plus off-site citation building, technical crawlability improvements, and integrated programme management. At Stefka, our AI SEO programmes include GEO as a core component — we don't treat them as separate services.
Ready to become the answer AI gives?
Tell us about your category and current search presence. We'll show you where you stand in AI search and what a GEO programme could achieve for your brand.