# aisearchoptimization.hu

> Optimize for AI answer engines like ChatGPT and Perplexity. Measure citability, reduce hallucinations, and fix infrastructure for generative search.

AI systems reconstruct experts rather than rank them. This site treats citability as an infrastructure problem requiring machine-readable, canonically anchored entities.

## Author

- Zoltán Varga — AI visibility researcher and AI ecosystem architect, Budapest: https://vargazoltan.ai/
- English profile: https://zoltanvarga.ai/en/about/
- ORCID: https://orcid.org/0009-0003-1020-834X
- arXiv author: https://arxiv.org/a/varga_z_1.html
- Zenodo: https://zenodo.org/search?q=owners%3A1653886

## Publications

- Per-Entity Bias Mapping for AI Visibility: Why Brand Mentions Require Entity-Specific Calibration (arXiv:2606.21595): https://arxiv.org/abs/2606.21595
- AI Visibility in the Generative Era: A Manifesto for Entity-Level Measurement (DOI 10.5281/zenodo.20285424): https://doi.org/10.5281/zenodo.20285424
- Quality Challenges Do Not Escalate Source Fabrication in Large Language Models (RICE-P1) (DOI 10.5281/zenodo.20474050): https://doi.org/10.5281/zenodo.20474050
- Rejection-Induced Confabulation Escalation — Pilot Study 2 (RICE-P2) (DOI 10.5281/zenodo.20509755): https://doi.org/10.5281/zenodo.20509755

## Key topics

- AI search optimization
- generative engine optimization
- AEO strategy
- LLMO techniques
- ChatGPT citations
- Perplexity optimization
- Google AI Overviews
- brand hallucination
- AI visibility audit
- machine-readable entities
- stochastic search
- GEO Hungary
- AI search trends
- optimizing for AI
- answer engine SEO
- AI citation errors
- AI search visibility
- generative AI audit

## Questions and answers

### How does AI search optimization differ from traditional SEO?

Traditional SEO targets ten blue links with deterministic rankings. AI search optimization focuses on how models reconstruct entities and cite sources in generative answers. There are no logged clicks or impressions in this new environment.

### What is the PEBM study finding regarding AI hallucinations?

The PEBM study ran 1,400 tests on 100 Hungarian B2B companies. It found that 45.9% of cited sources were fabricated overall, with higher rates for well-known firms.

### Why do well-known brands face more false attributions?

The brand hallucination hypothesis suggests familiarity gives models more material for plausible completions. This leads to confident but unsupported attributions for famous entities.

### Can AI crawlers access all pages on a website?

Access does not guarantee indexing. In one case, all pages returned HTTP 200 to bots, yet Google, Bing, and Brave indexed zero pages.

### Do search-based AI engines provide accurate dosage information?

With search enabled, engines provided officially approved doses in all tested instances. Without search, some models stated incorrect doses, creating brand risks.

### Is there a single number to measure AI visibility?

No single number exists because visibility is a distribution, not a point. Scores are proxies derived from multiple layers of output and processing.

## Related

- AI visibility expert page: https://vargazoltan.ai/ai-lathatosagi-szakerto/
- AI visibility audit: https://vargazoltan.ai/audit/
- Research: https://vargazoltan.ai/kutatas/