Work
Products, research and experiments — all in service of the same question: what makes content retrievable and citable by AI search systems.
Projects
Platform designed to analyze how websites perform in AI search systems and provide guidance for improving retrievability and citations.
System that analyzes semantic similarity across website pages using vector embeddings and cosine similarity to detect redundancy and topic drift.
AI-assisted workflow system for executing SEO tasks faster using modern AI tools combined with traditional SEO platforms.
Research
Introduces ISIC — a four-dimensional probabilistic visibility model (Inclusion, Stability, Influence, Coverage) — as the measurement framework for AI-driven discovery. Empirically validates that LLM and Google search channels direct buyers toward fundamentally different vendors, with an average Jaccard overlap of 0.03 across 27 B2B CRM queries.
Experiments
AI Citation Testing
Testing how different content structures influence whether AI systems cite a page in generated answers.
Retrievability vs Authority
Exploring why pages retrieved by search engines are not always cited by AI systems.
Semantic Content Mapping
Using embeddings to visualize relationships between pages and detect content gaps.