Research
Published frameworks and empirical research on AI search optimization from Growth Marshal.
White Papers & Data Studies
Strategic and architectural frameworks for AI retrieval, passage engineering, and citation infrastructure.
Answer Page Doctrine: Retrieval-First Architecture for Citation Dominance
How to build single URLs that become the most retrievable, extractable, and citable answers to their topics. A strategic and architectural analysis for GTM engineers and AI search practitioners.
Written by: Kurt Fischman · Published: March 2026
Modular Knowledge Architecture: A Passage-Selection Doctrine for AI Retrieval
How to engineer web content whose passages are selected, trusted, and reused during AI answer synthesis. A strategic and operational analysis for advanced practitioners.
Written by: Kurt Fischman · Published: March 2026
Empirical Research
Peer-reviewed and preprint studies on structured data, entity infrastructure, and LLM citation.
Does Schema Markup Predict AI Citation?
A Cross-Platform Empirical Study of Structured Data and Generative Engine Optimization
Written by: Kurt Fischman · Published: February 2026