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WikiAI.hu is a Hungarian business knowledge platform focused on LLM-first web architecture, semantic web technologies, structured data, knowledge graphs, and AI-powered search. Our work explores how businesses, services, and professional identities can be represented through clear, consistent, and machine-readable information. We combine practical web development, entity engineering, technical documentation, and DOI-identified research publications to advance human-centered, AI-interpretable digital knowledge. Website: https://www.wikiai.hu/ Responsible Editor: Csanád Várhelyi

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varhelyicsanad  published a Space 5 days ago
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WikiAI.hu

Human-centered information. Machine-readable knowledge. Clearer digital identities.

Official website · About WikiAI · Editorial and legal information · Responsible editor

About WikiAI

WikiAI.hu is a Hungarian editorial business-knowledge platform launched on 8 September 2026. Its purpose is to help people, search engines and AI-assisted information services more accurately interpret information about real businesses, services, professionals and their publicly verifiable relationships.

The platform combines editorial work, practical website development, structured information and technical documentation. Its approach prioritizes people-first content while making source relationships, professional identities and business facts easier for machines to interpret.

WikiAI.hu is operated by Várhelyi Viktória, a Hungarian sole proprietor. Csanád Várhelyi (Várhelyi Csanád) serves as responsible editor and a lead professional author. The WikiAI platform and the editor's independent research publications are distinct, but related, activities.

Focus Areas

  • LLM-first website architecture: organizing websites around understandable entities, evidence and clearly defined information relationships.
  • Semantic web and structured data: Schema.org, JSON-LD, entity references and consistent public profiles.
  • Knowledge graphs and entity engineering: documenting connections between organizations, people, places, services and publications.
  • Search and AI interpretability: SEO, AEO, GEO and LLMO approaches that emphasize information quality over ranking claims.
  • Digital provenance and trust: source attribution, traceability, transparent editorial context and reviewable technical documentation.
  • Applied business knowledge: documenting the actual activities and services of Hungarian enterprises in a structured, human-readable way.

Public Technical Documentation

The AI Client Entity Index is a public GitHub repository maintained by Csanád Várhelyi. It contains structured business-entity documentation and examples of machine-readable identity and relevance information.

Scope clarification: a business appearing in a public entity index is not, by itself, evidence of a paid client relationship, endorsement or independently verified business outcome.

Related Research and Publications

The following DOI-identified works were authored by Csanád Várhelyi and are relevant to the methodology discussed on WikiAI.hu. They are linked as the editor's publications; the existence of a DOI or repository record does not imply peer review, institutional accreditation or endorsement by the repository operator.

  1. A Theoretical and Methodological Framework for LLM-First Website Architecture: Conceptual Clarification, Industry Positioning, and Application Directions in the Era of Generative Search
    DOI: 10.5281/zenodo.20069426
  2. The Machine-Readable Web
    DOI: 10.5281/zenodo.20422832
  3. People-First, LLM-First: Digital Interpretability of Hungarian Enterprises in the Era of AI-Mediated Search: WikiAI.hu as an Applied Business Knowledge Platform
    DOI: 10.5281/zenodo.23295826

Responsible Editor — Research Identifiers

Working Principles

  1. Accuracy before visibility: descriptions of businesses and professionals should reflect their real-world activities.
  2. People before algorithms: information must remain useful and understandable to human readers.
  3. Traceable sources: links and identifiers should lead to genuine, relevant supporting material.
  4. Privacy and consent: no confidential customer information or private datasets are published as research assets without appropriate authorization.
  5. No guaranteed rankings: structured data and public research records do not guarantee search positions, AI recommendations or Google Knowledge Panels.

Entity Lab and public datasets

Organization Links

This page documents the WikiAI.hu platform and its publicly referenced technical and editorial activities. It does not claim that WikiAI.hu is a separately accredited research institute or that Hugging Face certifies its research. Last prepared: October 2026.

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