Latest research and insights on AI-first SEO, crawl optimization, and LLM seeding. Stay ahead of the curve with our comprehensive analysis of search engine evolution.
Understanding the sixteen-pillar model that defines on-page and off-page signals increasing AI engine citation likelihood. Based on comprehensive research analyzing 1,700 citations across four major AI engines.
Read ArticleHow large language models build ontologies and schema graphs for better content understanding. Explore the intersection of AI comprehension and structured data optimization.
Read ArticleHow publishers use structured metadata for semantic SEO in news media. Learn advanced techniques for optimizing content for both traditional search and AI-powered systems.
Read ArticleAdvanced OCR and AI data extraction techniques for turning PDFs into structured data pipelines. Transform unstructured content into AI-readable formats.
Read ArticleTracking topic drift in AI citations and maintaining content relevance over time. Understand how AI engines evolve their understanding of your content.
Read ArticleCurated list of open-source SEO tools you can actually use. Discover powerful tools for AI-first SEO optimization and structured data implementation.
Read ArticleOur research methodology combines academic rigor with practical implementation to deliver actionable insights for AI-first SEO optimization.
Systematic collection of AI engine citations, structured data implementations, and performance metrics across diverse industries.
Application of the GEO-16 framework to identify patterns and correlations between technical implementation and AI engine performance.
Rigorous testing of hypotheses through controlled experiments and real-world implementation across client projects.
Regular publication of findings with ongoing updates as AI engines evolve and new patterns emerge in search behavior.
Our research spans multiple domains within AI-first SEO, from technical implementation to behavioral analysis. Each category represents a critical aspect of optimizing content for AI engine comprehension and citation.
Crawl clarity engineering, URL optimization, site architecture, and performance metrics that impact AI engine accessibility and parsing efficiency.
Schema markup implementation, entity relationships, semantic understanding, and structured data validation for maximum AI comprehension.
LLM seeding optimization, entity clarity, content architecture, and semantic structure that enables AI engines to understand and cite content effectively.
Citation pattern analysis, entity recognition, content ranking factors, and behavioral modeling across major AI engines and search systems.
Our research directly informs the strategies and implementations that deliver measurable improvements in AI engine citation rates, user engagement, and organic visibility across diverse industries.
Average citation improvement within 90 days of implementing GEO-16 framework principles
AI engine citations analyzed across four major systems to develop the GEO-16 framework
Critical signals identified that determine citation success in generative search engines
Client satisfaction rate with AI-first SEO implementations based on our research
Get the latest insights on AI-first SEO optimization and join forward-thinking businesses preparing for the future of search. Our research-driven approach ensures you stay ahead of evolving AI engine behaviors and optimization opportunities.