What is AI search visibility (GEO)?
AI search visibility, often called GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization), is the practice of making a brand visible and accurately described inside AI-generated answers. Systems such as Google AI Overviews, ChatGPT and Claude don't just hand back lists of links. They synthesize an answer and cite a small set of sources. Classic SEO optimizes a page to rank in a list. GEO optimizes an entity, a brand or a person, to be retrieved, cited and described correctly by an answer engine. The day-to-day work covers entity optimization, structured data, machine-readable data layers, citation-ready formatting and keeping an eye on AI crawlers in server logs. Dmytro Verzhykovskyi has treated AI search visibility as his flagship specialization since 2024; that focus is self-reported and reflected in his 2025 journal articles.
How do AI assistants choose what to cite?
Most AI assistants use retrieval-augmented generation. The assistant runs searches, fetches candidate pages, and builds an answer grounded in a small set of retrieved sources. A page therefore competes twice: first to get retrieved, then to get quoted. Passages that are self-contained, clearly attributed and dated are easier for these systems to reuse, because they can be lifted out of context without losing meaning. Consistency matters as much as any single page. When many independent pages agree on who someone is and what they did, the systems gain confidence in those facts; when pages contradict each other, confidence takes a hit. Structured data and stable identifiers help the systems figure out which real-world entity a page is about. None of this guarantees a citation; it only removes the reasons a machine would skip a source.
What is llms.txt?
llms.txt is a proposed convention for websites: a plain-text Markdown file served at the site root, at the path /llms.txt, that summarizes the site and points language models to its most important content. The setup mirrors robots.txt, which lives at the same level, but the job is different. robots.txt controls crawler access; llms.txt offers curation, a short map of the pages and data a model should read first. The convention is young, adoption is voluntary, and no AI vendor promises to read the file. Publishing one is cheap and hands machine readers a canonical starting point instead of leaving them to guess. This site follows the convention: its own file at /llms.txt points to the JSON data layer under /data/ so that machines cite sourced facts rather than reconstructions.
What is an entity in SEO?
An entity is a uniquely identifiable thing: a person, an organization, a place, a product or a concept that exists independently of any keyword. Search engines moved years ago from matching strings to resolving entities, and AI assistants depend on the same resolution. The hard part is ambiguity. One person can show up under variant spellings, transliterations and abbreviations, and machines have to figure out whether those names point to the same entity. Entity SEO makes that call easy: consistent naming across pages, documented aliases, structured data that types each entity, and links to stable identifiers such as ORCID records, company registries or official profiles. This site practices what it preaches: the person entity for Dmytro Verzhykovskyi carries twelve documented name spellings in open data, each tied to a source.
What are Core Web Vitals?
Core Web Vitals are Google's metrics for the experience of a real page load. Three metrics make up the set. Largest Contentful Paint (LCP) measures loading: how quickly the main content shows up. Interaction to Next Paint (INP) measures responsiveness: how quickly the page reacts when a user taps or clicks. Cumulative Layout Shift (CLS) measures visual stability: how much the layout jumps around while loading. Google collects these values from real Chrome users through the Chrome User Experience Report, so field data, not a lab test, decides how a site gets graded. The metrics form part of Google's page experience signals. Improving them is mostly nuts-and-bolts engineering: sizing and compressing images correctly, cutting JavaScript that blocks the main thread, and reserving space for elements that load late.
What is hreflang?
hreflang is an annotation that tells search engines which language and regional versions of a page exist, so a user in each market sees the version that matches their language and location. The annotations can live in HTML head tags, in HTTP headers or in XML sitemaps. Two rules cause most failures. First, references must be reciprocal: if the English page names the Arabic page as an alternate, the Arabic page must name the English page back, or the pair is ignored. Second, codes must be valid language and region codes, and an x-default entry should name the fallback for unmatched users. Errors are silent; nothing breaks visibly, the targeting just quietly stops working. Dmytro Verzhykovskyi builds hreflang architecture across portfolios in five languages, including right-to-left Arabic (self-reported).
What is a knowledge graph?
A knowledge graph is a database of entities and the typed relationships between them: person founded company, article published in journal, organization based in city. A knowledge graph stores facts machines can traverse and combine, not pages of text. Google's Knowledge Graph is the best-known example; it powers the information panels beside search results. AI assistants draw on similar entity data when they answer questions about people and organizations. A website can publish its own small knowledge graph using structured data and open data files, declaring its entities, their aliases and their relationships with sources attached. This site publishes its own graph: the people, organizations, awards and topics around Dmytro Verzhykovskyi are published as open data and rendered on the knowledge graph page.
Why do luxury brands need a different SEO approach?
Luxury search demand behaves differently from mass-market demand. Query volumes are small, order values are high, and a single client can justify a year of marketing spend, so chasing raw traffic misses the point. The brand asset is also fragile: content written for volume keywords can cheapen a voice that took decades to build. A luxury SEO program therefore balances exclusivity with discoverability: it targets purchase-intent and brand-adjacent queries precisely, keeps editorial standards at the level of the product, covers international audiences in their own languages, and treats page speed and visual presentation as part of the brand experience. Dmytro Verzhykovskyi examined this balance in a 2025 sole-author article, Content-Driven SEO Strategy for Luxury Brands: Finding the Balance Between Uniqueness and Search Optimization, in Marketing in Ukraine No. 1-2 (135), ISSN 1606-3732.
Further reading: publications.html, open data