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Crawler Access · Indexing · Citability

AI Systems Reconstruct Experts
Not Rank Them

This site provides a fast check of crawler access, indexing, entity data and citability for AI models. AI visibility is stochastic; a single number serves only as a proxy for complex model behavior.

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

Core Audit Parameters

Three output layers define how AI answers appear to users.

Raw Output

The initial text generated by the model before any application processing. This layer contains the raw data models retrieve or fabricate.

App Processing

The intermediate step where software filters or formats the model response. This stage determines which facts reach the final interface.

User View

The final answer displayed to the human reader. This view often hides the underlying uncertainty of the source data.

Five Key Visibility Layers

These parameters measure how well a brand survives AI synthesis.

CrawlabilityHTTP status codes returned to AI bots like Googlebot and PerplexityBot.
Indexing DepthThe count of pages known to search engines versus those actually crawled.
Entity AnchoringMachine-readable data that links a brand to a canonical identifier.
Source FabricationThe rate at which models invent citations instead of using real URLs.
Context VariabilityHow answers shift based on user history and query phrasing.
Citability depends on whether a model can point to a machine-readable, canonically anchored entity.
StochasticAI answers vary by context and lack deterministic ranking.
InfrastructureVisibility is an infrastructure problem, not a marketing tactic.
ProxySingle-number scores only approximate complex model behavior.

Key topics

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

  • AI SEO check
  • AI visibility audit
  • crawler access test
  • entity data optimization
  • AI citability score
  • hallucination detection
  • LLMO strategy
  • GEO optimization
  • AI search indexing
  • brand reconstruction
  • AI SEO Hungary
  • crawling check
  • indexing issues
  • AI citation errors
  • perplexity audit
  • chatgpt sources
  • AI visibility index
  • machine readable entity

Questions and answers

Why do AI models invent sources for my brand?

Models reconstruct entities based on available training data rather than live verification. High-awareness brands often trigger more confident but false attributions due to the brand hallucination paradox. This occurs when familiarity provides material for plausible completions without factual support.

What is the difference between SEO and AI visibility?

SEO targets deterministic ranking algorithms while AI visibility addresses stochastic answer generation. AI systems do not log impressions or clicks, making traditional metrics unreliable. Optimization focuses on making content easy to quote and structurally sound for retrieval.

How do I check if AI crawlers can access my site?

Verify that all major AI bots receive HTTP 200 responses on your key pages. Indexing remains a separate bottleneck where engines may know zero pages despite successful crawling. Use tools that simulate specific AI agent behaviors to confirm access.

Can I improve my AI visibility score permanently?

No single number guarantees permanent visibility because model outputs are probabilistic distributions. Improvements involve fixing infrastructure gaps like entity anchoring and reducing source fabrication risks. Scores fluctuate based on model updates and query contexts.

What is the PEBM study regarding AI hallucinations?

PEBM analyzed 1,400 runs across 100 Hungarian B2B companies to measure source fabrication rates. It found that 45.9% of cited sources were fabricated overall, with higher rates for well-known firms. The study highlights the correlation between brand awareness and hallucination frequency.

Does paid advertising guarantee better AI citations?

The pay-to-appear hypothesis suggests a correlation but not causation between spending and citation quality. Models prioritize machine-readable entity data over commercial signals when constructing answers. Structural clarity matters more than ad spend for accurate reconstruction.

Author

Zoltán Varga

Zoltán Varga is the creator of the PEBM method and an AI visibility researcher who frames this field as an infrastructure challenge. His work on neural knowledge systems informs the audit logic used to measure how models reconstruct brands.

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 on AI visibility and the creator's profile remain on vargazoltan.ai.

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