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Entity Data · Schema · LLMs

Make Your Facts
Machine-Readable for AI

This site defines an entry-level package to make company facts consistent and machine-readable for AI systems. It solves the infrastructure problem where models reconstruct entities rather than rank them.

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

Core Components

Three elements ensure AI systems can cite your brand accurately.

Entity Data

Consistent facts allow models to anchor your brand correctly. This prevents confusion between similar names or entities.

Schema.org

Structured data helps AI parsers understand your business relationships. It provides the canonical links models require for verification.

llms.txt

This file guides large language models on what to read. It acts as a direct instruction set for AI synthesis.

AI Visibility Layers

Visibility depends on three distinct output stages in the generation process.

Raw Model OutputThe initial text generated before any application processing occurs.
App-Level ProcessingIntermediate steps where the system filters or formats the raw data.
User InterfaceThe final view the human sees, which masks the underlying complexity.
Context VariabilityVisibility is a distribution that changes based on user prompts.
Impression vs ClickAI answers create no logged impressions or traditional click metrics.
AI systems do not rank experts; they reconstruct them based on machine-readable anchors.
StochasticAI answers vary and cannot be predicted like SEO rankings.
InfrastructureCitation depends on data structure, not marketing spend.
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
  • machine readable facts
  • entity optimization
  • schema.org for AI
  • llms.txt guide
  • AI citation
  • brand hallucination
  • GEO strategy
  • AEO optimization
  • LLMO tactics
  • AI profile package
  • AI visibility audit
  • AI profil
  • gépi olvasható adatok
  • AI láthatóság
  • szintézis optimalizálás

Questions and answers

How does AI visibility differ from traditional SEO?

AI visibility is stochastic, meaning results vary with each query, unlike deterministic SEO rankings. Models reconstruct entities based on available data rather than following a fixed algorithmic ladder.

What is the PEBM study finding regarding citations?

The PEBM study found that 45.9% of cited sources in AI answers were fabricated overall. High-awareness firms saw a 52.7% fabrication rate compared to 37.9% for low-awareness firms.

Why might well-known brands face more hallucinations?

Well-known brands may receive more confident but false attributions due to the brand hallucination paradox. Familiarity gives models more material for plausible but unsupported completions.

What does an AI Profile package include?

An AI Profile package ensures company facts are consistent and machine-readable for AI systems. It covers entity data standardization, schema.org implementation, and llms.txt creation.

Can AI systems track clicks on their answers?

No, an AI answer creates no logged impression or click in the traditional sense. Measurement requires analyzing the three output layers from raw model text to the user interface.

What is the cost of measuring AI visibility?

Cost depends on scope: how many AI engines are tested, how many questions are asked and how often each question is repeated. A focused measurement can be run for a small budget; a full audit with repair work is scoped individually.

Author

Zoltán Varga

Zoltán Varga is the creator of the PEBM study and a researcher into AI reconstruction mechanisms. His work establishes that citability relies on machine-readable, canonically anchored entities rather than marketing efforts.

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, detailed research on AI visibility and the PEBM study is available on vargazoltan.ai.

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