Article Summary
Generative engine optimization (GEO) determines whether AI platforms name your business, present accurate information, and link to your pages.
Customers now rely on AI to make recommendations for products, so optimizing for AI visibility is more important than ever. GEO builds directly on solid technical SEO and expansive site coverage.
Optimist’s CORE Framework is a comprehensive model for measuring, understanding, and improving GEO performance across 21 key factors that drive AI visibility.
Generative engine optimization (GEO) is the strategy of ensuring AI tools mention your brand, accurately represent your offerings, and cite your pages and content. Platforms like ChatGPT, Perplexity, Claude, Gemini, and Google’s AI Overviews generate direct answers rather than providing a traditional list of links. GEO focuses on securing your place within those generated answers.
GEO is critical because buyers increasingly rely on AI to discover vendors. A G2 study of 1,076 software buyers revealed that 51% now start product research in AI chatbots rather than traditional search engines—nearly doubling from 29% in under a year.
What Is Generative Engine Optimization?
Generative engine optimization involves formatting your web content and digital presence so AI tools can find, accurately summarize, and cite your brand. Success is measured by your visibility in the final response delivered to the user.
GEO spans four key focus areas:
- Technical access: Ensuring AI crawlers can reach and render your content without relying on JavaScript.
- Topic coverage: Publishing comprehensive answers for every common buyer question, including technical or sales-focused details.
- Content structure: Formatting articles with clear, standalone sections that AI models can extract without losing context.
- Off-site presence: Aligning messaging across review platforms, media publications, and business directories with your main site.
All four areas depend on a well-indexed, crawlable website—the same foundation required for traditional SEO.
How Generative Engine Optimization Differs From SEO
While SEO and GEO rely on the same technical foundation, they differ in target outcomes, key metrics, and overall strategy.
| SEO | Generative engine optimization | |
| Goal | A page ranks and earns the click | A model names the brand or cites the page in its answer |
| Measurement and KPIs | Rankings, clicks, click-through rate | Brand mentions and AI citations across a tracked prompt set |
| Driving factors | Pages built for the query a person types | Pages optimized for model fan-out queries, exactable content structure, third-party presence across the web, and more |
Ranking a web page and securing a brand recommendation require different approaches. Search engines present list results for users to evaluate. Generative AI engines synthesize information, recommend specific vendors, and summarize key points—often satisfying the user’s intent without requiring a site click.
A marketing executive at an embedded finance firm highlighted this shift:
“I ask ChatGPT which vendors to evaluate. If our company isn’t included in that summary, we won’t even make the shortlist.
Traditional SEO groundwork remains vital. Clean crawlability, strong indexing, and thorough content coverage benefit both channels. However, SEO and GEO diverge in how content is selected, retrieved, and presented to users.
How GEO Works: The Two Arcs of AI Visibility
Two distinct retrieval systems shape how AI models generate answers.
The long arc represents what a model already knows about a category from its training data.
The short arc represents what it searches for in real time when a user asks a question.
Most answers rely on both systems, each operating on a very different timeline.
The Long Arc: What the Model Already Knows
The Long Arc reflects the model’s baseline understanding of which brands belong in a given category. It forms during initial model training and forms the basis for how LLMs and AI systems know and understand the world. Training data includes inputs from, essentially, all of the world’s content and most of the public internet.
This means your brand’s website content is baked into the training data along with reviews, roundups, press coverage, analyst reports, and company messaging across all public channels.
Everything in the training data influences AI outputs.
This effect is most evident in answers generated without live Web search.
In our analysis of 4,433 AI answers across 33 B2B categories, 110 of the 414 tracked brands never ranked in Google’s top 10 for a single target keyword. Yet 35% of those unranked brands were still mentioned in at least 20% of the AI answers in their category.
Existing training associations—not organic search rankings—likely explain these mentions.
Web-wide perception updates slowly. B
Building long-arc visibility is the work of over months, quarters, and even years.
Fundamentally, it’s a brand function — the more well-known your brand is writ large, the more surface area it occupies within the AI training data.
Key levers include earning third-party media coverage, gathering reviews, securing analyst citations, maintaining brand messaging consistency, and driving branded search volume.
The Short Arc: What the Model Looks Up
The Short Arc is the real-time information that models find and reference when you ask a question.
When you ask Gemini to recommend procurement software for a manufacturing company, it doesn’t rely solely on static memory. Instead, it executes real-time background searches.
Google refers to this process as query fan-out, explaining in its AI Mode update that the system runs “multiple related searches concurrently across subtopics and multiple data sources” before building a response. OpenAI uses a similar technique, noting that ChatGPT search “typically rewrites your query into one or more targeted queries that it sends [search] providers.”
The AI synthesizes its final answer (in part) from those internal search results.
The same basic mechanism (known broadly as retrieval-augmented generation, or RAG), is used by all major LLMs, including ChatGPT and Claude. In our analysis, we found that most commercial-discovery prompts kicked off 3-4 web searches.
Most AI responses combine both retrieval paths. Our analysis found that across 199 software buying prompts in 17 categories, ChatGPT performed a live web search for 91% of queries, relying solely on pre-trained memory for only 17 prompts.
Gemini performed live searches for 195 of the 199 prompts.
The key thing to recognize here is that each model has its own search behavior and it uses its own search patterns, which often differ from human search patterns.
This is one of the key reasons traditional SEO performance doesn’t always translate directly into AI visibility.
A business might hold a top Google rank for “best procurement software” yet remain unmentioned when a buyer prompts ChatGPT for specific vendor advice.
Optimist’s analysis found that pages cited in AI responses consistently show up in the Google search results for the fan-out queries used by the model.
This tells us that analyzing fan-out query patterns and targeting those specific topics is likely to help improve AI citation rates and potentially AI visibility as a whole.
Across cited sources in the study, 71% were vendor-owned properties, led by product and pricing pages (36%) and listicles or roundup pages (34%). Because models frequently include the current year in background sub-queries, recency plays a significant role in selection.
Short-arc optimization yields fast results. Once a page begins ranking for the sub-queries models run, it can start appearing in AI-generated answers within days or weeks.
How the Two Arcs Combine
In our analysis, holding a top-10 Google rank increased the probability of an AI mention by 1.8x. Still, 80% of brand mentions went to companies that did not rank on the first page for the initial prompt.
Both findings hold true because both retrieval systems operate simultaneously.
Pre-trained memory determines baseline brand recognition, while real-time retrieval selects which specific brands and pages earn citations in a given answer.
For most organizations, short-arc optimization provides the best starting point. It produces measurable results within a single quarter on owned web pages, demonstrating ROI to secure buy-in for longer-term brand-building initiatives.
Is Generative Engine Optimization a New Discipline?
GEO represents actionable, distinct tactics that build upon traditional SEO rather than replacing it. The primary shift lies in retrieval and outcome: AI engines evaluate pages the buyer never directly visits, delivering brand recommendations without requiring website traffic.
Much of the underlying execution remains familiar—relying on site crawlability, deep topic coverage, concise writing, and consistent off-site visibility.
GEO expands upon fundamental SEO by held to higher standards of clarity, structured coverage for AI-generated sub-queries, and tracking mentions instead of page positions.
How GEO Relates to AEO and AI Search Optimization
Generative engine optimization (GEO), answer engine optimization (AEO), and AI search optimization basically mean the same thing for our purposes. Wikipedia’s entry on GEO treats AEO, AI SEO, and LLM optimization as equivalent terms, noting the industry uses them interchangeably to describe the same core strategies.
Use whichever term fits your team’s existing workflow. Answer engine optimization and AI search optimization address identical objectives.
What Google Says About Optimizing for Its AI Features
Google detailed its perspective in its guide to AI features, stating: “From Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.”
This documentation focuses specifically on Google Search surfaces like AI Overviews and AI Mode, which rely on Google’s core ranking systems.
The guide highlights five tactics Google considers unnecessary for Google Search:
- llms.txt files or special markup: Standard crawling handles indexing.
- Content chunking: Google processes full web pages.
- AI-specific rewrites: Language models interpret natural synonyms.
- Artificial web mentions: Manufactured mentions offer minimal value.
- Excessive structured data: Schema helps but isn’t mandatory.
Google explicitly notes that llms.txt files “will neither harm nor help your site’s visibility or rankings in Google Search, as Google Search ignores them.”
However, Google represents only part of the AI search landscape. Data from Similarweb places ChatGPT at roughly 53% of generative AI website traffic, with Gemini around 27% and Claude at 9%. These external models source information independently of Google’s index.
The CORE Framework for Generative Engine Optimization
Optimist helps clients with GEO and AI search using our Complete Organic Revenue Engine (CORE) Framework.
Optimist CORE Framework evaluates AI search performance across 7 dimensions and 21 distinct factors that contribute to your brand’s AI visibility and recommendations:
- Technical Foundations: AI bots (GPTBot, ClaudeBot, PerplexityBot, Google-Extended) can access, crawl, and parse pages without JavaScript barriers.
- Content Coverage: Comprehensive pages answer core industry questions, including comparisons, pricing, and product features and use cases.
- Content Structure: “Chunkable” content that includes direct answers leading each section, followed by supporting context for easy AI extraction.
- Message Consistency: Product terminology, brand category, and positioning match across all published digital properties.
- Retrieval Signals: Content answers AI-generated sub-queries, stays regularly updated, and maintains structured knowledge-graph data.
- Authority: Third-party press, backlinks, and search demand validate market presence. Research by Ahrefs shows branded web mentions correlate with AI Overview visibility at 0.664, compared to 0.218 for backlinks.
- Proof: Verified reviews, case studies, and community discussion validate brand claims. AirOps research indicates 85% of brand mentions in AI answers stem from third-party sites rather than owned domains.
Each of these dimensions tells us something about the brand’s current presence and footprint and which factors are impacting AI visibility – both positively and negatively.
They also work as a general sequence and prioritization framework, telling us where to focus time and effort to drive the greatest impact.
For example, optimizing content structure yields results only after ensuring crawlers can access your pages and clear coverage exists for target topics.
Focus first on foundational requirements before adopting isolated tactics.
Working through superficial checklists without addressing foundational gaps leads to wasted effort on areas that are already performing fine.
How to Measure Generative Engine Optimization Performance
GEO measurement relies on two core metrics:
- Brand Mentions (Brand Recommendations or AI Share of Voice) — How often AI tools recommend your company directly, specifically in commercial discovery contexts.
- AI Citations — How often AI answers link to your site as a source.
Tracking both across a standardized prompt set—and correlating results with pipeline revenue—delivers a true measure of performance.
Google Search Console introduced generative AI performance reporting globally. As noted in Search Console’s documentation, these reports provide impression data for AI Overviews and AI Mode, though click-through metrics and specific query breakdowns remain limited.
Focus on measuring overall visibility trends across recurring query sets rather than relying on individual response outputs.
AI responses vary naturally across identical queries. Research by SparkToro and Gumshoe analyzed nearly 3,000 prompt iterations, finding that AI platforms produce identical brand recommendations less than 1% of the time across repeated runs.
AI tracking platforms monitor brand inclusions by running the same prompts through leading AI models over and over again.
Tracking visibility metrics alone provides limited value unless paired with actionable strategic insights.
Optimist’s GEO case studies show measurable revenue impact from investing in GEO to drive not only AI visibility but actual business outcomes.
By applying The CORE Framework, Optimist has helped many clients see significant improvements in AI search performance, such as:
- 49x growth in LLM referral revenue for a B2B tech client
- 8x increase in LLM conversions for a Fintech client
- 13x growth in LLM-driven revenue for a B2C ecommerce client
Each company is starting from a different point and AI responses in different industries and verticals are often driven by different factors and inputs.
This is why our framework is built to first understand the influences and factors that matter most to your business and your buyers and then build a custom-tailored strategy to drive GEO performance based on the data.
Where to Start With Generative Engine Optimization
Begin by seeing where you stand and building a roadmap.
Test 20 to 30 buyer prompts across ChatGPT, Gemini, and Claude to identify which vendors and sources appear.
Compare those results against your site’s existing content coverage and technical foundations, messaging clarity, and third-party presence quickly reveals priority areas for improvement.
Optimist’s CORE Analysis gives you a clear picture on both fronts and helps you see the exact steps you should follow to improve AI visibility, share of voice, and business outcomes.
If you want expert help charting your path, please reach out.
Schedule a consultation with Optimist.
Frequently Asked Questions About Generative Engine Optimization
Is Generative Engine Optimization Replacing SEO?
No, GEO complements SEO rather than replacing it. GEO relies on a well-structured, indexable site while extending optimization to address AI sub-queries, brand consistency, and citation presence. Search engines explicitly treat generative feature optimization as part of overall SEO strategy.
What Does Google Say About Generative Engine Optimization?
Google maintains that optimizing for AI features in Google Search is fundamentally part of standard SEO. Official guidance emphasizes focusing on overall content quality and user experience while noting that tactics like llms.txt files or AI-specific content rewrites provide no ranking advantage in Google Search.
How Do You Measure GEO?
Track brand mentions and AI citation sources across standardized buyer prompt sets over time. Because individual AI outputs vary, focus on visibility trends across query sets rather than static rankings, linking performance directly to conversion metrics.
How Long Does Generative Engine Optimization Take to Work?
Initial results often appear within 1 to 3 months, with compound impact building over 6 to 12 months. Technical and structural enhancements typically yield faster visibility gains, while expanding topic coverage and building off-site authority require longer timeframes to mature fully.
