Fabricated Sources
The PEBM study analyzed 1,400 runs across 100 Hungarian B2B companies. It found that 45.9% of cited sources were fabricated overall.
AI Visibility · Brand Citability · Hallucination Risk
Being Visible to AI
Reconstruction Over Ranking
AI systems do not rank experts; they reconstruct them based on machine-readable data. This site explains the infrastructure required for brands to be found, understood and cited by assistants like ChatGPT and Perplexity.
Visit vargazoltan.ai →More on this site soon. The work behind it is live on vargazoltan.ai.
Core Research Findings
Data from the PEBM study and live audits reveals how AI handles brand information.
Brand Paradox
Well-known brands often receive more confident but false attributions. Familiarity gives models material for plausible but unsupported completions.
Audit Results
A site can return HTTP 200 to every AI crawler and still stay invisible in AI answers. Access is the first layer; indexing, citation and accuracy decide the rest.
Key Parameters of AI Visibility
Visibility depends on five specific structural and behavioral factors.
Entity AnchoringModels require machine-readable, canonically anchored entities to reconstruct a brand accurately.
Output LayersThe process moves from raw model output to app-level processing before reaching the user.
Context VariabilityVisibility is a distribution per user, not a single static point score.
Search GroundingSearch-based models like Perplexity show lower fabrication rates than non-search models.
Crawl AccessibilityHTTP 200 responses do not guarantee indexing if bots cannot parse page content.
Make it easy to quote.
StochasticAI visibility is probabilistic, unlike deterministic SEO rankings.
InfrastructureCitation success relies on technical structure, not just marketing.
VerificationPre-registered protocols test rival explanations for model behavior.
Key topics
The terms this site covers, for readers, search engines and AI assistants alike.
- AI visibility
- brand citability
- AI hallucination
- GEO optimization
- AEO strategy
- LLMO
- answer engine optimization
- AI audit
- PEBM study
- fabricated sources
- AI citation
- machine readable entity
- AI visibility index
- Zoltán Varga
- AI kutatás
- márka láthatóság
- AI hallucináció
- keresőmotor optimalizálás
Latest articles
In-depth guides and analysis, each answering one question.
Explainer
AI Reconstructs Brands Instead of Ranking Them
Generative systems assemble brand identities from fragmented data, making traditional SEO metrics obsolete for AI visibility.
Analysis
Why AI Gets Famous Brands Wrong More, Not Less
Abundant data fuels fluent but factually incorrect answers about famous companies, creating a hidden reputational risk that standard metrics miss.
Analysis
PEBM Study: AI Fabrication Rates for Hungarian B2B Brands
Data from the Per-Entity Bias Mapping study shows that famous Hungarian companies are more likely to receive false citations from AI models than lesser-known firms.
Explainer
Why Small Companies Are Invisible to AI Assistants
Small firms face a structural invisibility failure mode where models lack the data density required to reconstruct the entity.
Concept
Ghost Cartography: How AI Maps Half-Known Entities
When AI systems encounter partial information, they fill gaps with plausible fiction rather than admitting ignorance.
Explainer
Type 3 and Type 4 Confabulation in AI Brand Representation
AI models do not just forget; they invent plausible but false histories for known entities and merge distinct people into single profiles.
Questions and answers
How does AI find my company?
AI systems reconstruct entities rather than ranking them. They rely on machine-readable, canonically anchored data to point to your brand. If this data is missing, the model may fabricate sources or omit you entirely.
What is the PEBM study?
PEBM stands for Per-Entity Bias Map. It involved 1,400 runs on 100 Hungarian B2B companies to measure source fabrication. The study found that over 45% of cited sources were incorrect across the dataset.
Do famous brands get better AI results?
Not necessarily. Well-known brands face a higher risk of confident but false attributions. Familiarity provides models with material for plausible but unsupported completions.
Can I improve my AI visibility score?
Single-number scores are proxies for complex distributions. Improvements require fixing crawlability, citation readiness and answerability layers. Technical access alone does not guarantee accurate citations.
Why do AI assistants invent facts?
Non-search models showed fabrication rates between 32% and 44%. Search-grounded models like Perplexity had lower rates at 7.32%. Lack of retrieval access increases the risk of hallucination.
Is AI visibility the same as SEO?
No. SEO targets deterministic ranking algorithms. AI visibility addresses stochastic reconstruction where answers create no logged impressions. The goal is to make content easy to quote accurately.
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 expands, related research and the full person profile remain on vargazoltan.ai.
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