15 AI Visibility and GEO Researchers and Practitioners to Follow in 2026

This guide profiles 15 people who publish evidence on AI visibility and GEO. GEO stands for generative engine optimization: improving how visible a brand or source is in answers written by AI systems such as ChatGPT, Gemini, Perplexity and Google AI Overviews. Each profile labels the type of evidence, summarizes the most useful finding and states the main limitation.

Disclosure: This guide is published by Rankfor.AI. Founder Dmitrij Żatuchin is included, and Rankfor.AI sells software and a managed service related to his research. His papers link to their public records so readers can inspect them directly. Apart from his featured section, people are grouped by the type of evidence they publish. The order is not a ranking, and inclusion does not imply endorsement.

Last checked: 4 November 2026. Roles, affiliations, publication records and the cited figures were rechecked against the linked sources on that date. They can change after it.

Dmitrij Żatuchin's research entry updated: 8 November 2026. It now includes his published Journal of Marketing Analytics article and four additional public preprints. Journal articles and earlier preprint versions of the same study are counted once.

The short answer

• Jason Barnard and Dmitrij Żatuchin are the two people in this group with verified, relevant, peer-reviewed journal articles: two applied framework papers by Barnard, and one empirical study by Żatuchin in the Journal of Marketing Analytics, published on 31 October 2026.
• Żatuchin's other thirteen studies covered here are preprints. Malte Landwehr and Tomek Rudzki share two SSRN working papers.
• A citation, brand mention, recommendation, and visit are different outcomes. Combining them into one visibility score hides useful information.
• Repeated prompts, model choice, language, location, and retrieval mode can materially change the measured result.
• Third-party pages supply most cited brand evidence in several large studies, while most public GEO evidence remains proprietary, observational, or commercially connected.

Why these 15?

Inclusion required a current AI-search role plus at least one identifiable public study, technical investigation, framework, scholarly paper, or operating product. Coverage is limited to these 15 and leaves many academic GEO authors outside its scope.

Each person was assessed on four questions:

1. Do they publish original measurements or system observations?
2. Is the method visible enough to judge the scope?
3. Are the data and limits stated?
4. Is the work an industry study, a public preprint, or a peer-reviewed article?

Żatuchin receives a paper-by-paper section because fourteen of his 2026 studies are the primary case study in this review. The other profiles use representative public work. Commercial affiliation matters because almost everyone here sells connected software, consulting, or agency services. Four people on this list work at Peec AI, and AEO Vision names two people on this list, İşler and Yeşilyurt, as its cofounders. Both companies sell AI visibility tracking software that competes with Rankfor.AI's own. Open methods, open data, replication, and careful attribution help readers judge those conflicts.

Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, and Ameet Deshpande introduced Generative Engine Optimization as a formal research framework in November 2023. Their GEO paper was later published at ACM KDD 2024.

AI visibility researchers and practitioners at a glance

Person	Follow for	Strongest public work type	Relevant scholarly status at the stated check date
Jason Barnard	Entity resolution and AI brand representation	Applied journal frameworks	Two peer-reviewed journal articles
Dmitrij Żatuchin	Buyer shortlists, sampling, multilingual measurement, and sources	Empirical journal article and preprints	One peer-reviewed journal article and thirteen further preprint studies; checked 8 November
Malte Landwehr	Prompt panels and cited-listicle exposure	Empirical working papers and vendor data	Two shared SSRN working papers; no peer-reviewed version located
Tomek Rudzki	Query fan-out and source selection	Empirical working papers and vendor data	Two shared SSRN working papers; no peer-reviewed version located
Metehan Yeşilyurt	Retrieval, reranking, and citation traces	Technical investigations and vendor data	No attributable scholarly paper located
David Konitzny	ChatGPT retrieval and page-reading traces	Product-specific technical investigation	No attributable scholarly paper located
Lily Ray	Search quality, trust, citations, and recommendations	Practitioner experiments	No attributable scholarly GEO paper located
Kevin Indig	AI traffic, citations, and business impact	Multi-study industry synthesis	No attributable scholarly GEO paper located
Aleyda Solís	International and multilingual AI-search measurement	Modeled traffic and citation studies	No GEO paper located; editor-reviewed 2020 Web Almanac chapter
Cyrus Shepard	Citation-factor evidence synthesis	Expert narrative synthesis	No attributable scholarly GEO paper located
Wil Reynolds	AI-assisted buyer research and business measurement	Small user study and agency research	No attributable scholarly GEO paper located
Ross Simmonds	Third-party distribution and source ecosystems	Large company study	No attributable scholarly GEO paper located
Mike King	Relevance Engineering and agentic retrieval	Practitioner framework and lab research	No attributable scholarly GEO paper located
Evan Bailyn	Commercial GEO strategy and buying-intent research	Proprietary agency studies	No attributable scholarly GEO paper located
Ipek Isler (İpek İşler)	AI visibility product operations	Operating product	No attributable scholarly paper or transparent primary study located

Evidence labels used in this guide

• Peer-reviewed: reviewed through the journal or conference's stated process.
• Preprint or working paper: public scholarly manuscript without verified peer review.
• Industry study: empirical work published by a practitioner or company.
• Technical investigation: observed traces, logs, or code from a specific product version.
• Framework or tool: an operating model or product without a methods-led validation study.

A DOI is a persistent identifier; peer review requires separate confirmation from a journal or conference. arXiv, Research Square, Zenodo, and SSRN host public research records under different processes.

Featured research program: Dmitrij Żatuchin's fourteen 2026 studies

Current role: Founder and CEO of Rankfor.AI. He was affiliated with the Estonian Entrepreneurship University of Applied Sciences until 3 November 2026; each paper keeps the affiliation it carried when published.

Best for: Understanding which buyer questions include your brand, which sources support it, and whether a change in an AI visibility report is worth acting on.

Strongest public evidence: A peer-reviewed empirical article in the Journal of Marketing Analytics, published on 31 October 2026, alongside thirteen further preprint studies. The work covers buyer shortlists, sources, languages, sampling, reliability and forecasting.

Start here if you lead marketing: read the journal study in item 2 for a way to examine the shortlist an AI answer gives a prospective buyer. Then use the language, source and repeat-measurement studies to choose what to test in your own market. An appearance in an answer is one step in the customer journey; enquiries, won customers and revenue need their own measures.

Żatuchin's 2026 research focuses on a measurement problem: an "AI visibility score" changes when the model, language, location, prompt, retrieval mode, repetition count, brand-extraction rule, or scoring tool changes.

That is a wider program than reverse-engineering one answer engine. It covers brand inclusion, citation sources, multilingual measurement, reliability, individual experts, buyer-prompt sampling, and contextual bias.

1. Gender framing changes the category and brand mix

Gender Bias in Large Language Model Brand Recommendations tested 1,279 public-version queries across Christmas and Valentine buying contexts. Female-framed Christmas prompts returned fewer brands, while the direction changed in the Valentine study on Gemini. Cross-model agreement on the actual brands ranged from zero to 0.68.

Plain language: answers to "for him" and "for her" questions concentrated in different product categories and brand pools. The pattern changes by occasion and model, so a single universal gender-bias rule would be misleading. This is a Research Square preprint, and no accepted peer-reviewed version was verified.

2. Measure the shortlist behind each buying question

The synthesised consideration set (Journal of Marketing Analytics, 31 October 2026) tested 250 queries across five industries and 50 sampled brands on three AI systems, with five repetitions in February and September 2026 (3,614 and 3,750 scored answers). Averaged across each industry, inclusion was spread fairly evenly among the sampled brands, yet 204 of the 250 questions had a brand named in at least 80% of answers. Brand shares in September tracked February closely (Spearman correlation 0.994), and reported cross-system agreement was 57.2% in February and 60.8% in September. Who Owns the AI Recommendation? is the earlier preprint.

Plain language: the useful unit is the individual buying question, because a brand can lead one question and be missing from the next. The paper measures which brands the answers supply. Whether buyers then consider or choose those brands needs separate evidence.

3. English-only monitoring misses local-market visibility

The Language Blind Spot collected 35,640 grounded answers for 66 brands across 11 markets, 12 languages, and three models. Local-language questions raised the recommendation share of local champions much more than that of global multinationals. Model choice still had a larger effect on overall stability.

Plain language: an English dashboard can make a local brand look weaker than it is. Market-language prompts reveal competitors and reputation patterns that English prompts leave out.

4. A small set of websites supplies much of the cited source base

How Large Language Models Source Brand Reputation Across Languages and Markets examined 167,551 URL-grounded citations across 128 brands. The merged corpus contained 13 languages; the main comparison covered 12, and Wikipedia led in 11 of those 12. About 18% of cited domains supplied 80% of all citations. Poland differed, with YouTube and careers sources playing a larger role.

Plain language: map the websites that keep appearing when customers ask about your market. The most frequently cited domains can help focus further research into relevant publishers, communities and sources; citation frequency alone does not show which source persuades a buyer.

5. An automated sentiment score can reflect the scoring tool as well as the answer

Query Language Sensitivity in Automated Sentiment Scores of LLM Responses, revised on 1 November 2026, examined 12,933 answers about 20 stored brand labels in eight languages across three provider aliases. English answers carried 591 negative classifier scores among 1,620 answers, against 77 among 11,313 answers in the other seven languages. The classifier was built for tweet sentiment, and English is the only study language in its documented sentiment fine-tuning data. The analysis cannot separate real differences in the answers from uneven scoring across languages.

Plain language: before acting on a sentiment chart split by language, check whether the scoring tool treats every language the same way. The study does not show that AI answers are more negative in English.

6. Fixed expert lists miss almost everyone an AI names

Who Gets Named used 2,400 grounded calls across 120 buyer questions, four models, four markets, five languages, and five repetitions. An individual professional appeared in 25.8% of answers. This is a lower-bound estimate because the high-precision detector recovered 61.7% of names in its validation sample. Category and model changed the rate sharply. A fixed list of 939 people matched only 0.47% of 27,293 name-shaped mentions, and only 26 people from that list appeared at all.

Plain language: a tracker built from a company roster sees a tiny slice of the people that AI may recommend. Open discovery is necessary when the research question is "who gets named?"

7. Buyer-prompt research needs a demand sample

Demand-Side Measurement for GEO, coauthored with Daniil Dzemesjuk, an AI/ML engineer at Rankfor.AI, introduces PersonaGen-1M: 1,031,732 synthetic buyer personas, 511 industry labels, four market contexts, 19.4 million structured attributes, and 5.16 million attached queries. Each persona carries an intent label and preferred source types.

Plain language: analysts often invent the questions used in an AI visibility audit. A large, structured prompt corpus provides a repeatable sampling frame. The personas are synthetic, and the dataset enables later tests; real buyer behavior remains untested.

8. Test language and location in the market you serve

The Language of the Question Selects the Market reports 234 retained runs through the logged-out ChatGPT interface and OpenAI API across four reported exit countries and six languages. Supplier mentions differed across the observed language and network contexts. Two paired probes changed the reported exit country while holding language fixed and returned different supplier sets. The small, non-randomised audit cannot isolate independent language or country effects.

Plain language: test the language and location your customers use. This one-provider audit gives a reason to check those conditions in your own market, without establishing a universal rule for local visibility.

9. The right repeat count must be estimated

The Dice Roll Method reanalyzed about 190,000 observations across more than 270 brands. Its corrected October 2026 version supports no fixed repeat count. Across a set of 250 questions, one or two runs per question already reached a reliability coefficient of 0.80. For five wordings of a single question on one model, reliability was 0.27, too uncertain to fix a run count. In simulations, detecting a large difference within one question with 80% power took 15 to 25 runs. A preregistered check on three independent datasets matched the method's predictions in 37 of 39 cells, with no failures.

Plain language: one answer is one draw from a changing distribution. Start with a small trial run, measure the variation, and calculate how many repeats the decision requires. A comparison across many questions needs few repeats per question; a claim about a single question needs many more.

10. How many brands a repeated audit finds depends on how it counts them

Measuring Brand and Source Discovery under Repeated LLM Queries collected 4,500 answers to 50 buying questions from six AI configurations, 15 times per question. With the original brand dictionary, ten calls recovered a median of 92.6% to 95.2% of the brands seen across all 15 calls. Re-checking all 45,683 candidate brand strings changed that range to 89.5% to 94.7%. The count of cited sources also changed when only the sources marked in the answer text were kept.

Plain language: a completeness figure from a repeated audit depends on how brands are extracted, which questions were chosen, and how many runs form the reference set. Report those choices with the figure. The study sets no universal stopping rule.

11. Check whether past brand mentions predict a later answer

Twenty Days Later reports a preregistered test of forecasts based on earlier brand mentions. Question-level history remained predictive when the same Gemini version was tested 20.3 days later, across twelve brands and three languages. This is an arXiv preprint.

Plain language: keep a history of answers to real buyer questions, then test whether it helps you anticipate later answers. This result concerns one model version and one follow-up period. It does not establish forecasts of customer demand or revenue.

12. A pattern in one category may fail in another

System Attribution in LLM Brand Recommendations found that individual answer text could strongly identify the tested AI system, while aggregated brand profiles did not transfer reliably across question domains. This is an arXiv preprint.

Plain language: a model's pattern of brand choices in one topic may tell you little about its choices in another. Before applying an AI visibility finding to your business, check your own category and buyer questions.

13. The writing tool alone does not justify another copy version

A Shared Taste for Model-Written Text reanalysed 21,828 text-comparison trials. It found a broad preference for model-written text but no detectable extra preference for a selector model's own output at the study's detection threshold. The SSRN record is another version of the same preprint study.

Plain language: this gives no basis to promise a brand advantage from using the same AI to write and judge a description. Test whether the copy helps a buyer understand the offer. The experiment measured text choices, not sales.

14. Repeated requests can keep returning a narrow set of choices

Ask a Language Model for Lottery Numbers examined 1,184 valid outputs. All six tested configurations returned concentrated, recurring number choices. This arXiv preprint tested number generation, not brand recommendations.

Plain language: asking again does not guarantee a fresh or representative sample. For a brand audit, that is a question to test directly by repeating buyer questions and recording what comes back; the number experiment does not establish the size of any brand effect.

What the studies mean for a marketing decision

Practical implication: start with a buyer question, record which firms and sources appear, and repeat the check under the conditions your customers use. Use the results to decide which product information, evidence or outside coverage needs attention. Rankfor.AI brings these studies together to guide those tests. Changes in AI mentions, customer enquiries, market share and revenue need separate measurement.

Limit or commercial interest: Rankfor.AI sells measurement software and a managed service related to this research. Thirteen of the fourteen studies are preprints, and several use proprietary collection systems or synthetic data. These studies do not establish that Rankfor.AI increases sales or market share.

Scholarly record: item 2 is a journal article. The Journal of Marketing Analytics received it on 27 June and accepted it on 20 October 2026, and the journal's submission guidelines state a double-blind peer review policy. Of the other thirteen studies, twelve have arXiv records and one has a Research Square record. The earlier arXiv version of the journal article and the SSRN version of A Shared Taste are not additional studies. Two earlier Research Square versions of the Dice Roll Method, rs-8980233 and rs-9496731, belong to the same work; arXiv version 2 supersedes both and corrects a note in version 1 that cited the gender-bias paper's DOI by mistake. Żatuchin has peer-reviewed publications outside this topic, including a 2024 Discover Education article. Rankfor.AI's publications page provides the current research index and publication status of each work.

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Peer-reviewed applied research

Jason Barnard

Current role: Founder and CEO of Kalicube.

Best for: Entity resolution, knowledge graphs, and the way AI systems represent people and companies.

Barnard's core practice framework separates understandability, credibility, and deliverability.

Strongest public evidence: Barnard has two verified peer-reviewed applied papers:

1. With Matt Artz, Search Marketing in the Age of AI, Journal of Digital & Social Media Marketing, 11(3), 244 to 260, 2023. It proposes a marketing framework for search, assistive, and answer engines. The publisher says every article is peer-reviewed, and the Aalborg University record classifies it as a peer-reviewed journal article.
2. Engineering the AI Résumé, Journal of AI, Robotics & Workplace Automation, 4(4), 338 to 355, 2026. It defines a framework for evaluating how AI represents an entity, includes falsifiable predictions, and gives a minimal replication protocol. The official journal volume lists the article, and the journal describes its anonymous peer-review process.

Practical implication: clear identity, corroborated claims, and accessible evidence make it easier for machines to resolve who a company is and what claims belong to it.

Limit or commercial interest: these are applied, practitioner-derived framework papers. Controlled retrieval experiments and independent validation of Kalicube's commercial method remain outside their scope.

Scholarly record: two verified peer-reviewed applied journal articles. Barnard also maintains a larger register of Zenodo and SSRN working papers; repository records with DOIs still require a separate peer-review label.

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Researchers with scholarly GEO working papers

Malte Landwehr

Current role: CPO and CMO at Peec AI.

Best for: Connecting product-scale answer-engine data with prompt panels and marketing questions that teams can test.

Strongest public evidence: Landwehr coauthored two 2026 SSRN papers with Jan Ehrlinspiel and Tomek Rudzki. Prompt Tracking Works, But Not as a One-Prompt Measurement System combines 288 human-written prompts with 1,466 controlled prompts: 54 base prompts and 1,412 variants. In results the authors call exploratory, the largest visibility differences came from prompts that differed most in meaning, prompt format shifted visibility baselines, and unbranded mid-funnel prompts were the most sensitive.

Cited-Listicle Rank-Tier Exposure, Author Type, and LLM Brand Visibility uses about 5.7 million brand-level observations across B2B software, emerging marketing technology, and US finance. Higher placement in frequently cited third-party listicles was associated with a higher chance of being named, and top-tier placement with earlier placement in the answer.

Practical implication: track a portfolio of prompts across distinct buyer needs, and treat frequently cited third-party listicles as measurable distribution surfaces.

Limit or commercial interest: the listicle study is observational, leaving the causal effect of moving a brand up one list untested. Landwehr and Rudzki work at Peec AI, which sells AI visibility tracking.

Scholarly record: two relevant SSRN working papers. SSRN's publication policy provides no platform peer review.

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Tomek Rudzki

Current role: GEO Expert at Peec AI. Before joining Peec AI, he cofounded ZipTie.dev, a tool for monitoring brand visibility in AI search.

Best for: Large-scale query fan-out and engine-specific source analysis.

Rudzki coauthored both SSRN working papers listed under Landwehr, Prompt Tracking Works, But Not as a One-Prompt Measurement System and Cited-Listicle Rank-Tier Exposure, Author Type, and LLM Brand Visibility.

Strongest public evidence: Rudzki's industry studies map retrieval at much larger scale. A five-million fan-out analysis found different research breadth by engine: Perplexity averaged 1.4 fan-outs, ChatGPT 2.1, and Grok 6.8 in fan-outs collected from 1 to 21 April 2026. Commercial modifiers such as "best," comparisons, reviews, tools, and features appeared frequently. A separate 30-million-source study showed that source preferences differ across engines.

Practical implication: the user's visible question is often the start of a larger hidden search process. Brands need coverage across the related questions an engine may generate, and each engine deserves its own measurement.

Limit or commercial interest: the large retrieval datasets come from Peec AI, where Rudzki works, and source behavior can change with product updates.

Scholarly record: two relevant shared SSRN working papers; no peer-reviewed GEO version was located.

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Technical and industry researchers

Metehan Yeşilyurt

Current role: GEO Researcher at Peec AI. AEO Vision names him as one of its cofounders.

Best for: Reverse-engineering fan-out, reranking, retrieval, and citation selection from product traces and large vendor datasets.

Strongest public evidence: Yeşilyurt's largest public study analyzed 64.77 million Reddit citations across 20 countries from ChatGPT, Google AI Overviews, Google AI Mode, and Gemini. These systems often cited machine-translated English Reddit pages in non-English markets, while ChatGPT's share changed sharply during the collection period. In a separate Perplexity check, no ?tl= citation was found. The result shows engine-specific behavior and fast drift.

His July 2025 Reciprocal Rank Fusion investigation reports RRF settings in code visible in ChatGPT's browser developer console. It supports a useful hypothesis: consistent appearance across several fan-out searches may beat one isolated first-place result.

Practical implication: measure whether a brand appears across related retrieval queries and engines. One isolated rank reveals little about the full answer process.

Limit or commercial interest: the largest dataset comes from Peec AI, where Yeşilyurt works. The code investigation is a product-version snapshot that he labels experimental, with no visibility into a permanent ranking formula.

Scholarly record: no attributable peer-reviewed paper, arXiv paper, or SSRN preprint was located. His public work is technical industry research.

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David Konitzny

Current role: GEO Researcher at Peec AI, after agency and in-house SEO roles.

Best for: Observing what ChatGPT Deep Research searches, reads, and rereads during a task.

Strongest public evidence: How ChatGPT Deep Research reads your site recorded WebSocket activity from more than ten free accounts. The study observed a search, open, and find sequence; an initial reading window around 5,700 characters; keyword-targeted rereading; and much less main content in the initial window on navigation-heavy pages. It also found that being read and being cited are separate events.

Practical implication: page order and clutter can affect what this research agent sees before it chooses sources.

Limit or commercial interest: this is a June 2026 snapshot from more than ten free accounts and about 20 requests per account. It covers ChatGPT Deep Research only: the post sets aside ChatGPT's quick Search answers and Agent mode, and it does not test Gemini or Perplexity. Konitzny works for Peec AI.

Scholarly record: no attributable relevant scholarly paper or preprint was located.

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Lily Ray

Current role: Founder of Algorythmic and Vice President, SEO and AI Search, at Amsive.

Best for: Search quality, trust signals, organic visibility, and the gap between an AI citation and a brand recommendation.

Strongest public evidence: In Why Calling Yourself the "Best" Could Be Helping Your Competitors Win in AI Search, Ray tested 100 B2B "best category" questions on three dates between April and June 2026. Eighty questions produced a Google AI Overview. Among 323 instances where an Overview cited a company's self-promotional listicle, the publisher's brand was absent from the recommendation in 224 cases, or 69%. In a September 2026 re-run that she reported on 4 October, the brand was left out 83% of the time, and AI Overviews cited 38% fewer of these listicles than in June.

Her study of an unconfirmed January 2026 Google update followed 11 selected subfolders and found that losses in Google visibility coincided with an average 22.5% fall in AI citations.

Practical implication: a company can supply the page that an AI cites while the answer recommends its competitors. Citation share and recommendation share measure different jobs.

Limit or commercial interest: the 11-subfolder sample is small and selected, so it supports a shared-signal hypothesis without proving a causal link. Ray sells consulting connected with the subject.

Scholarly record: no attributable relevant scholarly paper or preprint was located.

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Kevin Indig

Current role: Organic growth adviser and author of Growth Memo.

Best for: Connecting AI citations, search strength, traffic patterns, and business impact.

Strongest public evidence: Indig's talk Winners and Losers of the AI Disruption of Search draws on 20 months of his own user-behavior research, more than one million AI answers, and over 100,000 AI citations. It reports that ranking first in web search earned about four times as many AI citations as ranking tenth, and that only about 2.2% of citations persisted when the same prompt was run three times.

Semrush's Ghost Citations study, run in collaboration with Indig and Growth Memo, examined 3,981 domain appearances from 115 prompts across 14 countries and four AI search engines. It found that 61.7% were citations without a brand mention and 25.1% were brand mentions without a citation.

Practical implication: search strength still helps, while a citation, a mention, a recommendation, and a click remain four different outcomes. Reporting them as one score hides useful information.

Limit or commercial interest: the talk summarizes his own research and outside studies, and its page gives no method section. The Ghost Citations percentages come from 115 prompts and apply to that sample.

Scholarly record: no attributable peer-reviewed GEO paper or scholarly preprint was located.

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Cyrus Shepard

Current role: Founder of Zyppy and former Moz SEO lead.

Best for: Comparing many public citation studies and identifying claims that have support across several sources.

Strongest public evidence: AI Citation Ranking Factors Analysis reviews 54 selected experiments, studies, patents, and case studies, then scores 23 suspected citation correlates by hand, with AI help on the final numbers. The highest-rated signals include URL accessibility, ordinary search rank, fan-out rank, preview permissions, direct query-answer fit, appropriate content format, and placing the answer near the start of the page. The analysis finds little credible evidence for [llms.txt](/glossary/term/llms-txt/) as a citation lever.

Practical implication: pages that can be crawled, found, understood, and quoted cleanly tend to appear more often in AI citations. Shepard explicitly calls the items correlates and rejects the label of confirmed ranking factors.

Limit or commercial interest: the manual scoring is an expert narrative synthesis, outside the category of a registered systematic review or meta-analysis. Shepard sells SEO and AI search consulting.

Scholarly record: no attributable scholarly GEO paper or preprint was located.

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Aleyda Solís

Current role: International SEO and AI search consultant, founder of Orainti, and cofounder of Finchling.

Best for: Cross-market measurement, local search platforms, and turning AI presence into business reporting.

Strongest public evidence: Solís's global AI search study uses Similarweb estimates for 87.6 million click-producing AI referrals and 57,696 domain-market entries across ecommerce, finance, and travel in ten markets in March 2026. It shows that local platforms can outperform global defaults in their home countries.

An observational 15-ecosystem SaaS study covers three US software categories and combines Semrush and Similarweb estimates. It found that third-party pages supplied about 84% to 93% of citation weight in ChatGPT and AI Mode. The pages used as evidence were often different from the pages that received the visit.

Practical implication: international GEO needs local prompts and local source maps. The page that supplies evidence and the page that receives the customer can be different assets.

Limit or commercial interest: the traffic study relies on modeled Similarweb visits, and the SaaS study combines third-party estimates. Both are observational. Solís sells consulting connected with the findings.

Scholarly record: no attributable scholarly LLM-search or GEO paper was located. Solís coauthored the data-based 2020 Web Almanac SEO chapter, an editor-reviewed community research report outside conventional journal peer review.

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Wil Reynolds

Current role: Founder and CEO of Seer Interactive.

Best for: Connecting answer-engine measurements with customer behavior and business results.

Strongest public evidence: Reynolds and Andrea Haley ran 84 AI-assisted research sessions with 28 people. The report found large differences between the brands people would consider before the task and the brand they intended to buy after it. The study is small, and it shows why customer research needs outcomes beyond citation counts.

Seer also publishes larger studies through named research staff. Nick Haigler's query fan-out study ran 100 tracked prompts through Gemini 3's API twice a day for a week, 13 runs in all, and collected 11,029 unique fan-out queries. Only eight exact-match queries appeared in every run. Credit for that work belongs to Haigler and Seer.

Practical implication: track recurring needs and themes, then connect them to consideration, direct visits, signups, or sales. Exact hidden queries change too often to act like a keyword rank report.

Limit or commercial interest: the report does not say how the 28 participants were recruited. Its question before the task asked which brands people would consider, and its question after the task asked which one they now intended to buy, so the two answers are not like for like. Reynolds founded and leads an agency that sells related services.

Scholarly record: no attributable peer-reviewed GEO paper or scholarly preprint was located.

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Ross Simmonds

Current role: Founder and CEO of Foundation, also known as Foundation Marketing.

Best for: Distribution across the publishers, communities, videos, and professional networks that answer engines use.

Strongest public evidence: Foundation, which Simmonds founded, ran the Hidden Selection Phase study with AirOps. Over 60 days, from December 2025 to February 2026, it tracked 5.1 million responses and 57.2 million citations for 50 B2B brands across seven verticals and five AI platforms. Brand-owned pages supplied 10.15% of citations overall and 2.2% for unbranded discovery prompts. Reddit supplied about 20.8% of outside citations, YouTube 13%, and LinkedIn 11%.

Practical implication: AI systems usually meet a brand through somebody else's content. Distribution, public proof, and third-party description belong inside an AI visibility program.

Limit or commercial interest: the report is company research by Foundation, with data infrastructure from AirOps. The linked page summarizes a longer report, and both organizations have commercial interests related to the findings.

Scholarly record: no attributable scholarly GEO paper or preprint was located.

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Framework authors and product builders

Mike King

Current role: Founder and CEO of iPullRank. iPullRank credits him as the creator of Relevance Engineering.

Best for: Understanding AI search through information retrieval, technical access, entity resolution, and passage selection.

iPullRank's AI Search Manual, which lists King and five other contributors, connects information retrieval, technical access, entity understanding, content, digital PR, and measurement into one operating model.

His May 2026 essay Beyond RAG explains why many answer systems plan, search, inspect, and revise through several steps.

Strongest public evidence: an iPullRank analysis by Francine Monahan examined 79,000 URL-query pairs across ChatGPT, Claude, Perplexity, and Google. It found correlations between citations and search rank, topical match, entity richness, and concise coverage. The study should be credited to Monahan and iPullRank within King's broader framework.

Practical implication: diagnose discovery, retrieval, passage selection, and synthesis separately. A page can fail at any one of those stages before the user sees an answer.

Limit or commercial interest: the 79,000-pair analysis is correlational company research authored by Francine Monahan. King and iPullRank sell related agency services, and iPullRank became a Profound agency partner in July 2026. Profound sells AI visibility tracking that competes with Rankfor.AI's software.

Scholarly record: no attributable peer-reviewed GEO paper or scholarly preprint was located. The manual, essays, presentations, code, and lab work are practitioner publications.

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Evan Bailyn

Current role: Founder and CEO of First Page Sage.

Best for: Commercial GEO frameworks, buying-intent questions, and third-party evidence associated with recommendations.

First Page Sage sells GEO as an agency service and describes Bailyn as an early pioneer of the field. He publishes proprietary studies about commercial recommendations, citations, and traffic.

In a May 2023 post, Bailyn predicted a new agency service that he called Generative AI Optimization or AI Optimization. Aggarwal and colleagues formalized GEO as a research framework in November 2023. First Page Sage's GEO-titled page states an original publication date of 13 March 2024.

Strongest public evidence: First Page Sage says its GEO recommendation-factor study, last updated on 20 October 2026, covers 13,604 commercial questions across three chatbots. A separate buying-intent study reports 36,140 questions across four models and 17 industries. First Page Sage's proprietary framework associates authoritative third-party lists, review platforms, accreditations, customer evidence, and public sentiment with recommendations.

Practical implication: Bailyn treats GEO as a commercial demand channel and asks which outside evidence is associated with entry into a buyer's AI-generated shortlist.

Limit or commercial interest: the raw data, coding protocol, and full statistical model remain closed. First Page Sage also publishes a ranking of GEO experts that places its own founder first. We excluded those marketing pages from evidence of research standing.

Scholarly record: no attributable peer-reviewed GEO paper or scholarly preprint was located.

Follow: First Page Sage research · GEO study

Ipek Isler (İpek İşler)

Current role: Cofounder and CEO of AEO Vision. Her name appears as Ipek Isler in many English-language profiles.

Best for: Operating an AI visibility product that tracks prompt-level brand mentions, citations, sentiment, competitors, and changes over time.

Strongest public evidence: AEO Vision is a working product that tracks how AI answers mention and cite a brand and its competitors.

Practical implication: compare mentions, citations, sentiment, and competitors as separate prompt-level measures over time.

Limit or commercial interest: AEO Vision publishes two expert rankings that place its own cofounders in the top two places: İşler first on its AEO and GEO list, Yeşilyurt first on its SEO list. Both pages disclose the affiliation. That is circular promotional evidence and was excluded from the evidence used to establish research standing. The company's technical research is more directly attributable to Yeşilyurt.

Scholarly record: no attributable peer-reviewed paper, scholarly preprint, or transparent methods-led primary study was located. "AI visibility product builder and operator" is the accurate current label.

What this group has learned in plain language

One answer is too little for a market claim

Generated answers change from run to run. Five repetitions can support exploration in some settings, yet the required sample depends on the model, prompt, outcome, and size of the difference a team needs to detect.

Citation, mention, recommendation, and visit are separate events

A model can cite a page without naming its publisher. It can name a brand without linking to it. It can use one page as evidence and send the user to another. Each event needs its own metric.

Every engine is a separate channel

ChatGPT, Gemini, Perplexity, Grok, Claude, and Google AI surfaces use different search breadth, source mixes, and answer styles. An average across engines can hide the exact channel where a brand wins or disappears.

Language and location change the competitive set

Translated English prompts produce different results from local-language buyer questions. Query in the market language, capture the location, and keep those dimensions visible in reporting.

Third-party sources carry most of the brand story

Several independent and company datasets converge on the same broad direction: answer engines often rely far more on outside pages than on the brand's own domain. The useful source map includes publishers, review sites, communities, video platforms, directories, and local-market authorities.

Hidden retrieval widens the real query set

Answer engines often generate related searches behind the visible prompt. Strong coverage across the buyer's topic and adjacent questions improves the chance that at least one useful page enters retrieval.

Visibility needs a business outcome

Share of voice can show movement. Marketing still needs to connect that movement to corrected brand facts, shortlist inclusion, branded demand, direct visits, qualified leads, or revenue. A visibility chart without a decision attached will eventually lose executive support.

How to use this list

Following researchers is the starting point. Extract a testable claim, check whether its evidence matches your engine and market, reproduce it on your own prompt sample, and track the result over time. For a defensible AI visibility program:

1. Sample real buyer needs across the funnel and market. Prompts chosen in a conference room are too narrow.
2. Run each prompt several times on each relevant engine.
3. Keep language, location, model, date, retrieval mode, and account state in the record.
4. Report mentions, recommendations, citations, cited domains, sentiment, and visits as separate measures.
5. Use open brand extraction when the full competitor set is unknown.
6. Audit the outside sources that answer engines repeatedly use for the category.
7. Link visibility changes to a customer or commercial outcome.

Frequently asked questions

Which AI visibility researchers here have peer-reviewed papers?

Two people in this 15-person review have verified, directly relevant, peer-reviewed journal articles. Jason Barnard has two applied practitioner frameworks, which are separate from controlled information-retrieval experiments. Dmitrij Żatuchin has one empirical study of which brands AI systems include in their recommendations, published in the Journal of Marketing Analytics on 31 October 2026. Żatuchin founded Rankfor.AI, the publisher of this guide.

Which GEO researchers publish preprints or working papers?

Of the fourteen Żatuchin studies covered here, twelve are arXiv preprints and one is a Research Square preprint; the remaining study began as an arXiv preprint and is now a journal article. A Shared Taste also appears on SSRN and is counted once. Malte Landwehr and Tomek Rudzki coauthored two SSRN working papers with Jan Ehrlinspiel. No peer-reviewed version of those working papers was located by 4 November 2026.

What is the difference between a peer-reviewed paper, a preprint, and a vendor study?

A peer-reviewed paper has passed the review process stated by a journal or conference. A preprint is a public manuscript outside a verified journal or conference review record. A vendor study can contain strong empirical work, although access to its raw data, sampling frame, or full method is often limited. A DOI is a persistent identifier; peer-review status requires separate confirmation.

Who researches prompt stability and repeated sampling?

Żatuchin's Dice Roll Method and his repeated-query audit focus directly on repetition counts, reliability, and how many brands a repeated audit finds. Landwehr, Rudzki, and Ehrlinspiel study prompt portfolios and the effect of wording, meaning, funnel stage, and engine choice on visibility baselines.

Who studies query fan-out and answer-engine retrieval?

Rudzki publishes large query fan-out datasets. Yeşilyurt investigates reranking and retrieval traces. Konitzny observes ChatGPT's search and page-reading behavior. King explains the wider retrieval pipeline through Relevance Engineering and agentic search.

Who studies multilingual and international AI visibility?

Żatuchin studies language and location through controlled and multi-market datasets. Solís focuses on international traffic, local AI platforms, and cross-market source behavior. Both show that English-only monitoring misses important local variation.

Are AEO and GEO the same thing?

They are overlapping industry labels without a standardized boundary. AEO often refers to appearing as a direct answer. The original GEO paper defines methods for improving visibility in generative-engine responses. This guide uses AI visibility as the broader measurement category.

How were the people in this guide selected?

The guide uses the inclusion criteria in "Why these 15?" and groups people by evidence type. Many academic authors working on generative retrieval remain outside its scope.

Search method and limits

We checked primary author pages, original study pages, arXiv, Research Square, SSRN, DOI records, publisher pages, university repositories, and bibliographic indexes. Searches used name variants and combinations of each name with GEO, generative engine optimization, AI search, answer engine, LLM visibility, retrieval, and citation. On 4 November 2026 we rechecked the roles and cited figures against the linked sources and repeated the Crossref and arXiv author searches for all 15 names.

The field moves faster than journal publishing. Product behavior observed in June may change by September. Most large datasets are proprietary, full reproduction is unavailable for many vendor studies, and publication databases can miss obscure or newly indexed work.

"No scholarly paper located" is the result of a documented search through the cutoff date. An obscure or unindexed record may still exist. New journal versions should be added only after the publisher page or proceedings record confirms publication and peer-review status.

The strongest reading habit in GEO is simple: check who collected the data, what exactly was measured, whether the result is causal or correlational, and which evidence label belongs beside the claim.

Test the findings on your own brand

Several studies in this guide reach the same measurement lesson: one prompt is one observation. Use Answer Trail to compare AI answers to the same buyer question, the brands they name, and the sources they use.

Try Answer Trail

Answer Trail is built by Rankfor.AI, the publisher of this guide.

Teams that want the research and repeat checks run for them can see the scope of Rankfor.AI's managed AI search service, which covers one product or service in one language for 90 days.