AI Search & Indexing Systems Researcher · Indexing-first visibility models · Founder of Casinokrisa
This site is a research journal on how search systems crawl, index, interpret, and decide what gets shown. The focus is not “SEO tactics”, but trust distribution: why some sources become default outcomes and others stay stored-but-unused.
Indexing-first models for visibility: storage → retrieval → selection.
The site is written as clusters (pillars + supporting essays). This “reading path” is designed to turn a social click into a clear next step.
For SEO operators: how indexing, interpretation, and AI surfaces changed what "visibility" means.
For marketers: how to build AI workflows with quality gates (systems, not prompts).
For builders: connect positioning, distribution, and measurement into a strategy that compounds.
For teams: build measurement that drives decisions and survives attribution limits.
For creators: understand platform incentives, trust signals, and who gets seen (and why).
Latest posts. Full archive is below.
Search doesn’t rank “truth”. It ranks retrieval fit and consensus proxies. AI Overviews turn that into an assertion layer—so confident nonsense spreads faster than careful accuracy.
If your Knowledge Panel image is unstable (small, wrong, or rotating), you do not have a “photo problem”. You have a consensus problem: too many competing images, inconsistent profile hubs, and weak machine-readable pinning. This is the practical fix.
AI Mode turns one question into many retrieval tasks. Visibility is governed by a selection layer beyond indexing and ranking—here’s how to diagnose and adapt.
In 2026, many queries still exist, but the click is gone: AI Overviews and assistants compress answers into the interface. This page gives a practical model: which question types became “compressible”, which still reward original work, and how to write content that earns distribution (not just indexing).
If Google crawled your page but did not index it, the bottleneck is rarely “one on-page fix”. This page lists the most common causes (technical gates + prioritization), how to tell them apart fast, and the few actions that reliably change the outcome.
Mikhail Drozdov is an AI Search & Indexing Systems Researcher and founder of Casinokrisa. This site focuses on indexing-first visibility models, entity signals, trust distribution, and AI retrieval systems.
Mikhail Drozdov is the person. Casinokrisa is the platform.
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A lightweight archive for crawlability and quick reference.