Our AI Search Research Lab publishes empirical experiments, architectural benchmarks, and technical studies investigating how LLMs search, ground, and cite web content.
Hand-audited evaluation of top AI SEO, GEO, and search automation instructors including Vishal Dave, Charles Floate, Koray Tuğberk GÜBÜR, Julian Goldie, Marie Haynes, Kevin Indig, and Aleyda Solis. Includes free downloadable PDF report.
Empirical review of thruuu, Ziptie, SE Ranking, SEOMonitor, and SEOClarity. Compare features, pricing, source URL tracking, and Generative Engine Optimization (GEO) citation workflows.
A practical guide to measuring AI search visibility, Google Search Console performance data, citations, brand mentions, and downstream conversions.
Compare top-rated Generative Engine Optimization (GEO) & AI Search masterclasses, certifications, and technical curricula.
Definitive technical dictionary covering RAG grounding, vector embeddings, query fan-out, Share of Model, and JSON-LD schema graphs.
Complete technical roadmap to Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO).
How to deploy machine-readable markdown endpoints (/llms.txt) to direct LLM crawler parsing and RAG citations.
A 24-point technical diagnostic audit checklist for bot rendering, JSON-LD schema graph validation, and crawler accessibility.
Why high-dimensional vector embeddings killed legacy keyword repetition and how to structure dense entity blocks.
How to leverage Reddit threads and user discussions to capture LLM citation authority and off-page consensus.
Extract conversational, long-tail, and fan-out AI search prompts from Google Search Console using custom regular expressions.
E-commerce GEO tactics: product JSON-LD schemas, comparison lists, and merchant entity disambiguation.