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Audit · Measurement · Fixes

AI Systems Reconstruct Brands
Visibility Is Infrastructure

This site measures how AI models describe and cite companies. It treats citability as an infrastructure problem rather than a marketing challenge.

Visit vargazoltan.ai →More on this site soon. The work behind it is live on vargazoltan.ai.

Core Methodology Pillars

Three pillars define the audit approach to stochastic AI outputs.

Context Variability

Visibility is a distribution across users, not a single point. One answer does not represent the whole model behavior.

Output Layers

The audit tracks raw model output, app processing, and final user display. No logged impressions exist in standard AI answers.

Pay Hypothesis

We test if paid placement correlates with appearance frequency. Causality remains unproven in current synthetic data.

Five Key Parameters

These parameters define the scope of an AI visibility audit.

Raw Model OutputCaptures the initial text generated before any application filtering occurs.
App ProcessingRecords how the interface modifies or selects the raw generation for display.
User DisplayDocuments the final answer seen by the human observer.
Citation AnchorsVerifies if the model points to machine-readable, canonical entities.
Fabrication RateMeasures the percentage of false sources in the generated response.
Answer first. Make it easy to quote.
StochasticAI answers vary by query and context without fixed rankings.
InfrastructureFixes require canonical entity anchoring, not just keywords.
ProxySingle-number scores are estimates of complex distributions.

Key topics

The terms this site covers, for readers, search engines and AI assistants alike.

  • AI visibility
  • brand hallucination
  • AI audit
  • GEO optimization
  • AEO strategy
  • LLMO
  • AI citations
  • synthetic media
  • brand presence AI
  • ChatGPT audit
  • Perplexity accuracy
  • Google AI Overviews
  • AI visibility index
  • AI láthatóság
  • márkahallucináció
  • AI auditálás
  • AI hivatkozás
  • generatív AI

Questions and answers

How do you measure AI visibility?

We run repeated queries across multiple models to map output distributions. The audit analyzes raw generation, app processing, and user display layers to calculate fabrication rates and citation accuracy.

What is the PEBM study?

PEBM stands for Per-Entity Bias Map and analyzed 1,400 runs on 100 Hungarian B2B companies. It found that 45.9% of cited sources were fabricated overall, with higher rates for well-known firms.

Do AI models rank experts like Google?

No, AI systems reconstruct experts based on available data rather than ranking them. Citability depends on whether a model can point to a machine-readable, canonically anchored entity.

Why do famous brands get wrong citations?

The brand hallucination paradox suggests familiarity gives models more material for plausible but unsupported completions. Well-known brands may receive confident but false attributions due to this effect.

Is SEO enough for AI visibility?

SEO targets deterministic search algorithms while AI visibility addresses stochastic synthesis. Optimizing for AI requires making content easy to quote and structurally sound for model ingestion.

What costs are involved in an audit?

Costs depend on the number of queries and models tested, such as the 650-answer crop-protection study. Specific pricing varies based on the depth of the 25-dimension audit required.

Author

Zoltán Varga

Zoltán Varga is the creator of the PEBM study and the Neural Awareness framework. He defines AI visibility as an infrastructure problem requiring canonical entity anchoring.

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.

vargazoltan.ai →

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.

Research papers and the person profile remain on vargazoltan.ai. The AI Visibility Index and workshop details are in preparation on this domain.

Go to vargazoltan.ai →