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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.

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Core Research Findings

Data from the PEBM study and live audits reveals how AI handles brand information.

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.

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

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.

Author

Zoltán Varga

This domain focuses on AI visibility research led by Zoltán Varga, creator of the PEBM study. His work analyzes how AI assistants reconstruct brands and the infrastructure needed for accurate citation.

AI visibility researcher and AI ecosystem architect based in Budapest. Author of the Per-Entity Bias Mapping study on arXiv (2606.21595) and of the RICE studies on source fabrication in language models. He builds his own research system (GFIS), personal and organizational knowledge bases, and AI agents, and teaches leaders how to work with AI.

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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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