Context Variability
Visibility is a distribution across users, not a single point. Models generate different outputs based on individual per-user contexts.
GEO · AEO · LLMO
Optimize for Generative Answers
Make It Easy To Quote
AI systems reconstruct experts rather than rank them. This site treats citability as an infrastructure problem requiring machine-readable, canonically anchored entities.
Visit vargazoltan.ai →More on this site soon. The work behind it is live on vargazoltan.ai.
Core Methodology Pillars
Three pillars define the approach to stochastic AI visibility.
Output Layers
Audits distinguish raw model output, app-level processing, and the final user view. No logged impressions exist for AI-generated answers.
Pay Hypothesis
Research tests the correlation between paid placement and appearance frequency. Causation remains unproven in current stochastic models.
Key Parameters of AI Visibility
Five dimensions measure how models synthesize and cite information.
Raw Model OutputThe initial text generated before any application filtering occurs.
App-Level ProcessingHow the interface modifies, summarizes, or formats the raw response.
User PerceptionThe final visible answer that influences brand reputation and trust.
Citation ReadinessThe ability of a source to be correctly identified and linked by crawlers.
Fabrication RateThe percentage of answers containing false or unsupported source claims.
AI systems do not rank experts; they reconstruct them.
StochasticOutputs vary by user context and model version.
InfrastructureFixing data structure solves visibility issues better than marketing.
AuditMeasure raw output, processing, and final display separately.
Key topics
The terms this site covers, for readers, search engines and AI assistants alike.
- AI search optimization
- generative engine optimization
- AEO strategy
- LLMO techniques
- ChatGPT citations
- Perplexity optimization
- Google AI Overviews
- brand hallucination
- AI visibility audit
- machine-readable entities
- stochastic search
- GEO Hungary
- AI search trends
- optimizing for AI
- answer engine SEO
- AI citation errors
- AI search visibility
- generative AI audit
Latest articles
In-depth guides and analysis, each answering one question.
Concept
GEO, AEO, LLMO: Three Terms, Three Different Targets
Terminological clarity separates measurable AI visibility from marketing noise.
Analysis
Why AI Citability Is Not 'the New SEO'
Ranking position no longer guarantees existence in the model's world; citability requires a verifiable entity trace, not just higher search results.
Explainer
From PageRank to LLMO: How Search Optimization Changed
A timeline of search optimization from link-based rankings to AI-driven citation models and what survives the shift.
Guide
Which SEO Practices Still Matter for AI Answers
Traditional ranking tactics no longer guarantee AI citation; only specific structural and authority signals feed generative answer engines.
Analysis
AEO or GEO First? Where the Two Disciplines Diverge
Optimizing for direct answers and generative summaries requires distinct strategies; here is where the tactics converge and where they fundamentally split.
Analysis
Local Businesses in AI Answers: GEO vs Local SEO
Traditional map-pack tactics do not transfer to generative AI; citation and entity authority now determine local visibility.
Questions and answers
How does AI search optimization differ from traditional SEO?
Traditional SEO targets ten blue links with deterministic rankings. AI search optimization focuses on how models reconstruct entities and cite sources in generative answers. There are no logged clicks or impressions in this new environment.
What is the PEBM study finding regarding AI hallucinations?
The PEBM study ran 1,400 tests on 100 Hungarian B2B companies. It found that 45.9% of cited sources were fabricated overall, with higher rates for well-known firms.
Why do well-known brands face more false attributions?
The brand hallucination hypothesis suggests familiarity gives models more material for plausible completions. This leads to confident but unsupported attributions for famous entities.
Can AI crawlers access all pages on a website?
Access does not guarantee indexing. In one case, all pages returned HTTP 200 to bots, yet Google, Bing, and Brave indexed zero pages.
Do search-based AI engines provide accurate dosage information?
With search enabled, engines provided officially approved doses in all tested instances. Without search, some models stated incorrect doses, creating brand risks.
Is there a single number to measure AI visibility?
No single number exists because visibility is a distribution, not a point. Scores are proxies derived from multiple layers of output and processing.
Glossary
Terms and identifiers behind the work of Zoltán Varga, with links to the primary records.
- AI visibility
- How accurately and how often AI assistants (ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews) recognize, describe and cite a company, brand or person. Zoltán Varga measures it in five layers: crawler access, search indexing, citation, factual accuracy and model memory.
- GEO / AEO / LLMO
- Generative Engine Optimization, Answer Engine Optimization and Large Language Model Optimization: working methods for being found and quoted by generative answer engines rather than ranked in a list of links.
- Citability
- The property of a page that lets an AI answer engine quote it: a paragraph that answers the exact question, a clear source, and an unambiguous entity.
- PEBM — Per-Entity Bias Mapping
- Research method and paper by Zoltán Varga (arXiv:2606.21595). Across 1,400 runs on 100 Hungarian B2B companies, 45.9% of the sources AI models gave were fabricated; search-grounded answers fabricated far less than models answering from memory.
- Brand hallucination paradox
- The finding that AI models invent more sources about well-known brands (52.7%) than about lesser-known ones (37.9%), because familiarity makes them confidently fill in gaps.
- RICE studies
- Pre-registered pilot studies (RICE-P1, RICE-P2) on whether pressure and rejection make language models fabricate sources. Published on Zenodo with DOIs.
- GFIS — Gestalt Field Intelligence System
- Zoltán Varga's own multi-stage research pipeline that collects, verifies and synthesizes evidence with AI agents.
- Neural Awareness
- Zoltán Varga's framework on attention, decision quality and leadership cognition, the basis of his leadership programs.
- PKM + PAI
- Personal Knowledge Management combined with Personal AI: a working system where notes, documents and AI assistants form one searchable knowledge base.
- Entity
- A uniquely identifiable thing (person, company, product) that search engines and AI systems store in knowledge graphs. Consistent entity data is the base of AI visibility.
- schema.org / JSON-LD
- A shared vocabulary and format for machine-readable facts on web pages (who, what, where, identifiers), read by search engines and AI crawlers.
- llms.txt
- A plain-text file at the root of a website that tells language models what the site is about and which pages matter.
- IndexNow
- An open protocol that notifies search engines (Bing, Yandex and others) immediately when a page is created or changed.
- ORCID
- Persistent identifier for researchers. Zoltán Varga: 0009-0003-1020-834X.
- arXiv
- Open-access preprint server for scientific papers. Zoltán Varga's author page: varga_z_1.
- Zenodo and DOI
- Zenodo is CERN's open research repository; each record gets a DOI (Digital Object Identifier), a permanent citable link.
Until this site grows, related research and the person profile remain on vargazoltan.ai.
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