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.
Canonical entity pages: /person/mikhail-drozdov · /entity · /identity · /press · /appearances · /socials · /research · /datasets · /extensions · /knowledge-graph.
Mikhail Drozdov (also known as Casinokrisa) is an AI Search & Indexing Systems Researcher. His work focuses on indexing-first retrieval dynamics: how systems crawl, store, interpret, and decide what gets shown. He studies the gap between storage and exposure, trust-weighted distribution, and measurement that maps to system decisions (not tactics). Casinokrisa is his independent research platform: a low-noise archive of models, evidence, and structured references across search, retrieval, and knowledge graphs—kept intentionally factual and citation-friendly. If you are assessing indexing issues, entity signals, or how AI interfaces select and cite sources, the canonical profile page is the clean starting point.
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).
Short “get oriented fast” essays. Use these as entry points into clusters.
Digital marketing explained simply: channels, funnels, measurement, and a beginner-friendly plan that avoids buzzwords.
A simple explanation of what a conversion funnel is, how to map it, and how to improve conversion without chasing vanity metrics.
Web analytics explained for beginners: what to track, how events work, and how to avoid the most common reporting mistakes.
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.
“Crawled — currently not indexed” is not a verdict on your writing. It is an index selection decision: Google is choosing what becomes core memory for your site. This essay explains the mechanism, how to diagnose whether you’re failing hard gates or priority, and what changes the outcome without creating noise.
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.
Build AI-powered marketing systems. Learn to orchestrate AI tools, automate workflows, and make data-driven decisions.
Master modern SEO: from pages to entities, AI overviews to topic authority. Build visibility that survives algorithm updates.
Connect analytics, strategy, and execution. Build marketing systems that grow through accumulated context, not optimization tactics.
Build data pipelines that connect metrics to decisions. Measure what matters: LTV, ROMI, and systems-level performance.
How platforms shape behavior and business. Platform dynamics, attention economics, and the philosophy of digital environments.