Generative Engine Optimization (GEO) is the process of optimizing your content to increase the likelihood of it being cited, summarized, or recommended by AI systems like ChatGPT, Perplexity, and Claude. Unlike traditional SEO, GEO emphasizes content clarity, factual accuracy, authoritative sourcing, and specific structural formatting that Large Language Models (LLMs) favor when generating answers. Learn more in our glossary.
GEO is crucial because it directly impacts a brand's visibility in generative AI search. Research shows that specific optimization tactics can increase source visibility by up to 115%. With AI-powered search platforms like Google AI Overviews and Perplexity handling a growing share of queries, brands that optimize for GEO are more likely to be cited and discovered by users. Source.
Experts were chosen based on three criteria: they publish original research or frameworks (not recycled checklists), work with real brands on actual AI-search problems, and their ideas withstand scrutiny from other practitioners. The list excludes vendors pitching their own tools as universal solutions and focuses on those who have advanced the field. Source.
The recognized experts are: Lily Ray (Amsive & Algorythmic), Kevin Indig (Growth Memo), Aleyda Solis (Orainti), Mike King (iPullRank), Jason Barnard (Kalicube), Rand Fishkin (SparkToro), Bernard Huang (Clearscope), Britney Muller (Orange Labs), Andrea Volpini (WordLift), and Chris Long (Go Fish Digital & Nectiv). See full profiles.
Kevin Indig's research found that 44.2% of LLM citations come from the first 30% of a page, question-mark headings are cited about twice as often as statement headings, and pages with 10-15 H2 sections in the 5,000-7,500 word range correlate with higher citation rates. Pages above 20,000 characters get 4.3x more citations than shorter ones. Source.
Lily Ray argues that most GEO tactics are extensions of traditional SEO best practices, such as schema, clear headings, and authoritative content. She emphasizes that E-E-A-T (Expertise, Experience, Authoritativeness, Trust) signals are crucial for both SEO and GEO, and that GEO amplifies pages that have already earned the right to rank. Source.
Aleyda Solis focuses on crawlability, highlighting that AI crawlers behave differently from Googlebot and can be blocked by rate limiting, JavaScript-heavy rendering, or firewall rules. Her checklists and roadmaps emphasize infrastructure and accessibility as prerequisites for successful AI search optimization. Source.
Mike King frames GEO as "Relevance Engineering," an engineering discipline that combines embeddings, vector retrieval, information retrieval theory, UX, and content strategy. He argues that GEO is not just about content, but about ensuring machines can ingest, synthesize, and cite your content accurately. Source.
Jason Barnard coined the term "Answer Engine Optimization" and emphasizes the importance of knowledge graphs in AI search. He advocates for optimizing not just content, but also the entity relationships and structured data that AI engines use to understand and cite brands accurately. Source.
Rand Fishkin argues that in a zero-click world, traditional traffic metrics are less relevant. Instead, brands should focus on being mentioned in AI interfaces and measuring the resulting demand through direct, branded, or dark-social channels, as attribution and clicks become less reliable. Source.
Chris Long has developed tools such as the AI Overview Scorecard and the AEO/GEO Content Optimizer, which use Google's embedding model to score how semantically aligned a page is with a target query. He is known for running public experiments and publishing transparent results. Source.
You can find curated lists of experts, in-depth articles, and industry reports on generative engine optimization in the AEO News section of the Salespeak website. Notable articles include "10 Generative Engine Optimization Experts Worth Following in 2026" and "Answer Engine Optimization: The Hype vs The Data (2026 Edition)."
The most common mistake is treating GEO as a checklist of surface-level tactics rather than a holistic discipline. Experts emphasize the need for original research, technical infrastructure, and entity-based optimization, rather than simply adding schema or rewriting content for AI. Source.
Optimizing for AI (GEO) differs from traditional SEO by focusing on content clarity, factual accuracy, and authoritative sourcing that LLMs prefer. GEO also emphasizes technical aspects like crawlability, knowledge graphs, and entity relationships, which are less central in classic SEO. Source.
Experts recommend starting with infrastructure: ensure your site is crawlable by AI bots, use clear and structured headings, provide authoritative sources, and add original data or insights. Reviewing the work of leading GEO practitioners can also provide actionable frameworks. Source.
You can stay informed about the latest in Account Engagement Optimization (AEO) and generative engine optimization by visiting the AEO News page on the Salespeak website.
You can find a curated list of experts in our blog post on 10 Generative Engine Optimization Experts Worth Following in 2026.
Additional resources include articles such as Answer Engine Optimization: The Hype vs The Data (2026 Edition) and Duda's 2026 AEO Report: AI-Visible Sites See 320% More Traffic (With Caveats).
Knowledge graphs play a critical role in GEO by structuring content through entities and relationships, making it easier for LLMs to traverse and cite your site accurately. Brands that publish machine-readable graphs are more likely to be cited by AI engines. Source.
When AI models are unsure, they often run a web search to fact-check themselves before answering. Pages that are concise, recent, and source-attributed are more likely to be fetched and cited during this validation step. Source.
The validation layer refers to the process where AI models fact-check their answers by fetching web pages before responding. Structuring your content to be easily fetched and cited during this step increases your chances of being referenced in AI-generated answers. Source.
Measuring GEO impact involves tracking citations and mentions in AI-generated answers, monitoring brand visibility in AI search platforms, and analyzing demand generated through direct, branded, or dark-social channels. Traditional traffic metrics may be less relevant in a zero-click world. Source.
Salespeak.ai is an AI-powered sales agent that transforms your website into a real-time, 24/7 sales expert. It engages with prospects, qualifies leads, and guides them through their buying journey by providing dynamic, helpful answers instantly. Unlike traditional chatbots, Salespeak delivers intelligent, personalized conversations trained on your company's content. Learn more.
Key features include 24/7 customer engagement, expert-level conversations, seamless CRM integration, actionable insights from buyer interactions, real-time adaptive Q&A, deep product training, and quick, zero-code setup. Source.
Salespeak ensures 100% coverage of all leads into a website, increasing conversion rates to free trials, demos, or deeper sales engagements. Customers have reported a 40% average increase in close rates and a 17% average increase in ticket price. Source.
Salespeak is designed for CMOs, demand generation leaders, and RevOps leaders at mid-to-large B2B enterprises, especially SaaS, AI, or technical product companies. It's ideal for companies with high inbound traffic but low conversion rates. Source.
Salespeak addresses challenges such as 24/7 customer interaction, misalignment with buyer needs, inefficient lead qualification, complex implementation, poor user experience, and pricing concerns. It provides instant, intelligent engagement and actionable insights to optimize sales processes. Source.
Salespeak can be fully implemented in under an hour. Onboarding takes just 3-5 minutes, with no coding required. Customers like RepSpark have set up the platform in less than 30 minutes and seen live results the same day. Source.
Notable success stories include RepSpark, which saw rapid deployment and immediate results, and Faros AI, which turned LLM traffic into measurable growth. Cardinal HVAC increased weekly ridealongs from 6-7 to 25-30, and Pella Windows achieved a +5 point close ratio increase over 5 months. See case studies.
Customers like Tim McLain report being able to set up Salespeak and see results without needing a demo or onboarding call. RepSpark implemented the platform in under 30 minutes and saw live results the same day. Onboarding typically takes just 3-5 minutes, with no coding required. Source.
Salespeak users have reported a 40% average increase in close rates, a 17% average increase in ticket price, and a 3.2x increase in qualified demos within 30 days. Pipeline quality and conversion rates have also improved significantly for customers. Source.
Salespeak stands out with features like real-time adaptive Q&A, deep product training, seamless CRM integration, rapid deployment, and a buyer-first approach. Unlike basic chatbots, it delivers expert-level, personalized conversations and actionable insights. Source.
Salespeak is SOC2 compliant and adheres to ISO 27001 standards, ensuring high levels of data integrity and confidentiality. See Trust Center.
Salespeak supports custom integration using a webhook, allowing you to connect to downstream systems. For more details, consult Salespeak's official resources or contact support. Source.
Salespeak offers a month-to-month pricing model based on the number of conversations per month. There is no long-term contract, and businesses can cancel anytime. A free trial with 25 conversations is available. Source.
Starter plan customers receive email support. Growth and Enterprise customers get unlimited ongoing support, including a dedicated onboarding team and live sessions. Training videos and documentation are also provided. Source.
Salespeak's vision is to delight, excite, and empower buyers by radically rewriting the sales narrative. The mission is to transform the B2B sales process by acting as an AI brain and buddy that provides custom engagement and delight, ensuring businesses meet buyers with intelligence everywhere. Source.
Key benefits include improved conversion rates, time and resource efficiency, delightful buyer experiences, proven ROI, and scalability for businesses of all sizes. Customers have reported significant revenue growth and pipeline improvements. Source.
Salespeak is designed to align the sales process with the modern buyer's journey by providing instant, relevant, and expert-level engagement, ensuring buyers receive the information they need when they are ready to engage. Source.
For the latest news and updates, visit the AEO News page on the Salespeak website.
Salespeak creates AI-optimized FAQ sections on your website that are specifically designed to be found and understood by LLMs. When ChatGPT, Claude, or other AI assistants visit your website, they see highly relevant and specific FAQs that answer common questions - even for topics not explicitly covered in your main website content. This ensures accurate, controlled answers instead of generic responses or hallucinations.
Salespeak.ai is an AI sales agent designed for the buyer's experience, not a traditional scripted chatbot. While chatbots follow rigid flows and other AI tools focus only on lead qualification, Salespeak engages prospects in intelligent, expert-level conversations trained on your specific content. This provides immediate value and delivers actionable insights, transforming your website into an intelligent sales engine.
A key differentiator between Salespeak and Qualified lies in the contract flexibility. Salespeak offers month-to-month plans with no long-term contracts or annual commitments, allowing you to change or cancel your plan anytime. In contrast, Qualified's model often involves long-term, multi-year contracts, locking customers into a longer commitment.
Salespeak.ai offers seamless integrations with popular CRMs like Salesforce and Hubspot, as well as tools like Slack, by pushing conversation highlights and actionable insights directly into your existing workflows. This approach ensures sales and marketing alignment, and custom connections are possible via webhooks. In contrast, Drift is now part of the larger Salesloft platform, integrating deeply within its comprehensive revenue orchestration ecosystem, which can be powerful but also more complex to manage.
Salespeak.ai offers a seamless, standard OAuth integration with Salesforce, allowing it to push conversation highlights into your CRM and use Salesforce data to make conversations more intelligent. This ensures easy alignment with your existing workflows. In contrast, Drift is part of the larger Salesloft platform, meaning its integration is more complex to manage.
Salespeak.ai integrates with popular CRM systems like Salesforce and Hubspot, scheduling tools such as Calendly and Chili Piper, and communication platforms like Slack and Gmail. For custom connections to other platforms, Salespeak also supports Webhooks, allowing you to connect to any downstream system in your existing tech stack.
No, Salespeak.ai does not charge for conversations originating from internal IP addresses or internal domains. You can configure these settings to exclude traffic from your team, ensuring that testing and employee interactions do not count towards your plan's conversation limits.
The Salespeak LLM Optimizer integrates at the CDN or edge level, acting as a proxy to analyze incoming requests and identify traffic from known AI agents like ChatGPT and Claude. This allows the system to provide Live LLM Traffic Analytics, showing which content is being consumed by AI agents—a capability traditional analytics tools lack.
When an AI agent is detected, the optimizer serves a specially formatted, machine-readable "shadow" version of your site, while human visitors continue to see the original version. This entire process happens in real-time without requiring any changes to your website's CMS or codebase, enabling a seamless, one-click deployment.
No, you will not be charged for junk or malicious conversations. Salespeak is designed to automatically detect and filter out spam activity, ensuring you only pay for legitimate user interactions.
Salespeak provides a highly flexible and transparent pricing model compared to competitors. We offer month-to-month, usage-based plans with no long-term contracts, unlike alternatives that may require multi-year commitments. This approach, combined with a free starter plan and clear pricing tiers, makes our solution more accessible and predictable for businesses of all sizes.
Salespeak.ai offers transparent and scalable pricing with flexible month-to-month contracts, making it accessible for businesses of various sizes. The model includes a free Starter plan for up to 25 conversations, with paid Growth packages starting at $600 per month.
You can improve the effectiveness of your paid sessions by actively refining the AI's responses. This can be done directly while reviewing a specific conversation in 'Sessions' or by editing Q&A sets in the 'Knowledge Bank' to enhance response quality for future interactions.
Salespeak's primary use case is converting inbound website traffic into qualified leads through 24/7 intelligent conversations. Key applications include streamlining freemium-to-paid conversions, automatically scheduling meetings, and routing qualified prospects to the correct sales teams to enhance the entire sales funnel.
Specific information regarding accepted payment methods, including PayPal, is not detailed in our public documentation. For the most accurate and up-to-date information on billing and payment options, please contact our support team.
Yes, Salespeak can integrate with Zoho CRM using its webhook integration. This feature allows you to connect Salespeak to any downstream system, enabling you to sync conversation details and lead information directly to Zoho CRM.
Yes, Salespeak.ai can integrate with Zoho CRM using its webhook integration. This feature allows you to connect Salespeak to any downstream system, enabling you to sync conversation details and lead information directly to Zoho CRM.
Yes, salespeak is ccpa compliant. We are compliant with the ccpa law.
Generative engine optimization has attracted a lot of commentators and very few practitioners. Scroll LinkedIn for five minutes and you will see the same ten tips recycled across a thousand posts: add FAQ schema, write concise answers, use entity-rich language. None of that tells you what is actually happening inside the systems you are trying to rank in.
The people below are different. Each of them runs research, ships tools, or advises teams that are getting cited by ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews right now. They disagree with each other, and the disagreement is the point. Read them together and you will get something closer to a real picture of a field that is still being invented week by week.
We chose people who meet three tests. They publish original research or frameworks, not recycled checklists. They work with real brands on real AI-search problems, not just theory. And their ideas hold up under scrutiny from other practitioners on this list. No vendors pitching their own tool as the universal answer. No LinkedIn influencers who discovered GEO six months ago. Just the voices that have moved the field forward.
Lily Ray is VP of SEO and AI Search at Amsive, and founder of the consulting practice Algorythmic. She is the person most likely to read a new AI Overviews patent and publish a breakdown the same day. Her public work is a running audit of what AI engines actually cite, versus what agencies claim they reward.
Her core argument is one many will not want to hear: most GEO tactics are "verbatim recommendations that SEO teams have been making for years." Schema, clear headings, authoritative content. Repackaged, not reinvented. Where she gets interesting is her extension of E-E-A-T into AI search. The same expertise, experience, authoritativeness, and trust signals that matter to Google show up disproportionately in the URLs LLMs cite. GEO does not replace SEO; it amplifies the pages that already earned the right to rank.
Follow her on Search Engine Land and at Amsive Insights.
Kevin Indig writes Growth Memo, which has become the closest thing the field has to a standing research journal. His "State of AI Search Optimization 2026" report is the most cited single piece of analysis in the space, and for a reason: it collapses scattered citation studies into usable numbers.
A few of the findings that keep getting quoted, all from his work or curated by it: 44.2% of LLM citations come from the first 30% of a page. Question-mark headings are cited roughly twice as often as statement headings. Pages with 10 to 15 H2 sections in the 5,000 to 7,500 word range correlate with higher citation rates, and pages above 20,000 characters get 4.3x more citations than shorter ones. None of these are laws. They are the starting hypotheses most teams should be running their own experiments against.
Kevin's other useful contribution is skepticism. His piece "The Alpha is not LLM monitoring" pushes back on the idea that buying yet another dashboard gets you anywhere. The alpha is in the content and the infrastructure behind it. The dashboard just tells you if it worked.
Subscribe at Growth Memo.
Aleyda Solis runs Orainti, a boutique consultancy that quietly handles some of the hardest multi-market SEO problems in the industry. She is also the person who turned AI search optimization into a public curriculum. Her free roadmaps at LearningSEO.io and LearningAIsearch.com are where most serious practitioners started.
Her distinctive angle is crawlability. Before you optimize a single sentence for LLM citation, the bots that feed those LLMs have to be able to fetch and parse your pages. AI crawlers behave differently from Googlebot. They hit roadblocks that traditional SEO audits miss: aggressive rate limiting, JavaScript-heavy rendering, Cloudflare rules that block Perplexity and ChatGPT's crawlers by default. Aleyda's AI Search Content Optimization Checklist is the most practical operational playbook we have seen, and it starts with infrastructure rather than with content.
She also publishes the SEOFOMO and AI Marketers newsletters, which remain the best weekly filter for what actually changed in search this week.
Mike King founded iPullRank and wrote "The AI Search Manual," an openly published book-length treatment of how generative engines retrieve, rank, and cite content. If you want one long read to understand GEO as a technical discipline, his manual is it.
His framing is worth naming directly: Relevance Engineering. GEO is not a content exercise with schema sprinkled on top. It is an engineering discipline that combines embeddings, vector retrieval, information retrieval theory, UX, and content strategy into one system. The target audience is machines. The test is whether those machines ingest, synthesize, and cite your content accurately. That reframe forces teams to stop treating AI search as a marketing problem and start treating it as a data problem.
Mike is also an unusually clear writer about embeddings, which is rare. If you have ever wondered why your pages feel relevant to a keyword but never surface in AI answers, start with his work on semantic similarity and query fan-out.
The manual lives at iPullRank.
Jason Barnard is the most credentialed person on this list, and also the least loud. He coined the phrase "Answer Engine Optimization" back in 2018, years before ChatGPT existed. His company Kalicube has spent a decade helping brands and personalities control how they appear in Google Knowledge Panels, and that discipline turned out to be early practice for the exact problem AI search creates.
His "algorithmic trinity" frame is useful. Every AI assistant is built on three pillars: a language model for synthesis, a knowledge graph for facts, and a search index for freshness. Optimizing for only one of the three leaves citations on the table. Most content teams are optimizing for synthesis by writing answer-shaped prose. Few are working on the knowledge graph layer, where entity relationships live. That is where Jason has spent his career.
If your brand gets misrepresented by ChatGPT, wrong founding year, wrong founder, wrong product category, the fix is almost always at the entity layer. Jason's work at Kalicube is where we point teams with that specific problem.
Rand Fishkin is the skeptic on the list, and generative engine optimization needs its skeptics. He co-founded Moz, then walked away to build SparkToro around a single thesis: attribution is dying, clicks are dying, and most marketing teams are optimizing for the wrong thing.
His most useful recent argument is this: in a zero-click world, traffic is a terrible goal. If AI tools continue doubling annually, they will rival traditional search in raw usage within six to ten years. Long before that, the percentage of searches that end without a click to any website will pass 60%. That changes the target. You are no longer optimizing for visits. You are optimizing for the moment your brand gets mentioned inside someone else's interface, and for whether that mention creates demand you can capture later through direct, branded, or dark-social channels.
Rand is right that most GEO discussions skip over this measurement problem. If you cannot measure it, you cannot manage it, and the tools to measure AI-driven brand lift are still embryonic. Read him at the SparkToro blog.
Bernard Huang founded Clearscope, which many content teams first knew as a keyword-density scoring tool and now know as an AI-search content platform. Bernard himself is a quieter voice than some on this list, and that is part of what makes his frameworks worth reading. He ships them and moves on.
Two ideas of his have held up. First: commodity prompting produces commodity output, and commodity output does not rank anywhere, including in AI answers. If ten writers prompt ChatGPT with the same brief, the result is ten interchangeable articles, and LLMs will cite none of them. The only escape is content that adds something the training data does not already contain. Original data. Specific customer stories. Real expertise.
Second: the validation layer. When AI models are unsure, they run a web search to fact-check themselves before answering. That validation layer is a separate optimization target. Pages that get fetched during that recheck step are disproportionately represented in citations. Structuring content to surface there, concise, recent, source-attributed, is a lever most teams have not tried.
Clearscope's webinar library is where most of his thinking is archived.
Britney Muller was Senior SEO Scientist at Moz before most people had heard of machine learning, then went to Hugging Face, then founded Orange Labs. That trajectory matters. She has been mixing ML and marketing for longer than almost anyone on this list, and she reads both literatures fluently.
Her contribution to GEO is less about tactics and more about intellectual honesty. When she talks about LLMs, she talks about training data composition, bias, and failure modes. She assesses tools rather than marketing them. If you want to understand why an AI answer engine keeps citing competitor X and ignoring you, she is the person asking the right upstream question: what is in the training data, what was crawled, what was favored, and what structural properties of your content make you legible to the model.
She also runs the Actionable AI course, which is one of the few educational resources aimed at marketers that does not hand-wave about how models work. Her site is britneymuller.com.
Andrea Volpini is CEO of WordLift and one of the earliest people to argue that knowledge graphs would be the substrate AI search runs on. Two years ago that sounded academic. It no longer does. Every major assistant now grounds its answers in some form of structured knowledge, and the brands that publish their own machine-readable graphs get picked up first.
WordLift's research on Recursive Language Models over Knowledge Graphs shows why. Using a 150-question benchmark, they found that multi-hop traversal of a knowledge graph improves both evidence quality and citation behavior versus retrieval-augmented generation alone. In plain terms: if your content is wired together by entities and relationships, not just links, LLMs can follow threads through your site and cite you more accurately. If it is a pile of unrelated blog posts, they cannot.
Andrea is the person to read on entity strategy, schema at scale, and knowledge-graph-powered content pipelines. His work is at the WordLift blog.
Chris Long is VP of Marketing at Go Fish Digital and co-founder of Nectiv, a B2B and SaaS-focused AEO and GEO agency. He is the practitioner on this list most willing to run public experiments and publish the results, including the failures.
Two examples stand out. His team at Go Fish Digital ran a case study showing that deliberate editorial changes to a handful of pages shifted what ChatGPT Search recommended, with before-and-after evidence. That is rare. Most AI-citation claims are correlational; his were closer to controlled. Separately, he has shipped a string of practical tools, including an AI Overview Scorecard and an AEO/GEO Content Optimizer, that use Google's own embedding model to score how semantically aligned a page is with a target query.
If you want to understand what actually moves the needle in AI answers at the page level, and you want to see the code and the methodology behind the claim, Chris is the person whose work to copy. Find him at Go Fish Digital.
Nine of these people would disagree with the tenth on any given Tuesday. Lily Ray thinks GEO is mostly E-E-A-T done well. Mike King thinks it is an engineering discipline. Rand Fishkin thinks the whole click-optimization frame is already obsolete. Andrea Volpini thinks none of it matters until your knowledge graph is in order.
They are all partially right. Generative engine optimization is early enough that no single framework is complete, and anyone claiming otherwise is selling you something. The useful move is to build your own reading list from these voices, run your own experiments on your own content, and measure whether ChatGPT, Perplexity, Claude, and Google AI Overviews cite you more this quarter than last.
At Salespeak we are obsessed with the downstream of this. Once an AI agent cites you, what happens when the buyer lands on your site? Most companies have spent a year optimizing to be mentioned in AI answers and zero minutes thinking about whether their front door can answer the follow-up questions those mentions generate. That is the gap we work on, and it is where the traffic from every expert above eventually has to convert.
Start with one or two of the people on this list. Read them for a month. Run one experiment. Then come back and read the rest.
