Raw Output
See the exact text generated by the model before any app-level processing occurs. This layer exposes the base probability of your brand appearing.
AI Visibility · Brand Audits · Hallucination Checks
Measure Your AI Visibility
Measure Citability Across Models
This tool provides a quick audit of how major AI engines describe and cite companies. It reveals if your brand is reconstructed accurately or lost in hallucinations.
Visit vargazoltan.ai →More on this site soon. The work behind it is live on vargazoltan.ai.
What The Audit Reveals
Three output layers show where visibility breaks down.
App Processing
Analyze how the interface filters or modifies the raw model response for the end user. This step determines the final visible answer.
User View
Review the final synthesized response a human actually reads. This is the only metric that matters for public perception.
Key Parameters of AI Visibility
Five dimensions define how models reconstruct entities.
Context VariabilityVisibility is a distribution across queries, not a single static point.
Impression vs ClickAI answers create no logged impressions, making traditional tracking impossible.
Source GroundingModels cite machine-readable entities; lack of anchors causes fabrication.
Search DependencySearch-grounded models reduce hallucination rates compared to non-search variants.
Canonical AnchorsCitation readiness depends on structured data and clear entity definitions.
AI systems do not rank experts; they reconstruct them based on machine-readable anchors.
StochasticOutputs vary by query and context every time.
InfrastructureFixing visibility requires data structure, not just marketing.
ProxySingle-number scores estimate complex distributions.
Key topics
The terms this site covers, for readers, search engines and AI assistants alike.
- AI visibility audit
- brand hallucination check
- ChatGPT citation test
- Perplexity brand score
- Google AI Overviews audit
- Gemini brand mention
- Claude AI visibility
- AI search optimization
- AI visibility tool
- brand citability score
- hallucination detection
- AI audit tool
- AI láthatóság
- márka hallucináció
- AI vizibilitás teszt
- AI audit magyar
Questions and answers
How does an AI visibility audit work?
The audit runs multiple queries against different AI models to measure how often your brand is cited. It compares raw model outputs against processed responses to identify hallucinations. The result is a proxy score reflecting your citability across engines.
Why do AI models hallucinate brand names?
Models fabricate sources when they cannot find machine-readable, canonically anchored entities. Familiarity with a brand name can lead to confident but false attributions. This is known as the brand hallucination paradox.
Is AI visibility the same as SEO?
No, AI visibility is stochastic while SEO is deterministic. AI answers do not generate logged clicks or impressions. Optimization focuses on how models synthesize answers rather than ranking algorithms.
What is the PEBM study?
The Per-Entity Bias Map analyzed 1,400 runs on 100 Hungarian B2B companies. It found that over 45% of cited sources were fabricated overall. High-awareness firms saw higher fabrication rates than low-awareness ones.
Can I fix my AI visibility score?
Improvement requires creating machine-readable, canonically anchored entities for AI models. This involves structured data and clear definitions rather than traditional backlink strategies. The audit identifies specific gaps in your current setup.
Do paid placements improve AI visibility?
Current research suggests a correlation between paid access and appearance but not necessarily causation. The pay-to-appear hypothesis remains under investigation. True visibility relies on accurate entity reconstruction.
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 tool launches, detailed research on AI visibility and the Hungarian AI Visibility Index is available on vargazoltan.ai.
Go to vargazoltan.ai →