Meet the Team

This resource didn't emerge from a marketing department. It came from practitioners who noticed a gap between what content teams were being told about AI and what was actually observable in search performance data. The team here brings together backgrounds in editorial content, technical SEO, content strategy, and AI tool evaluation. None of us claim to have all the answers — the landscape is genuinely evolving. What we can offer is careful, grounded analysis that tries to be honest about what's known, what's uncertain, and what's still being figured out.

Maya Okonkwo

Lead Researcher, Search Quality

Maya spent a decade as a content director before moving into search quality research. Her focus is on how algorithmic systems evaluate content characteristics — particularly the signals that separate genuinely helpful pages from optimized-but-hollow ones. She reads quality evaluator guidelines the way other people read novels. Her work forms the analytical backbone of this resource.

Daniel Reyes

SEO Analyst and Content Strategist

Daniel came to SEO through journalism, which gives him an unusual lens on content quality. He's particularly interested in the gap between what content teams think search engines value and what the observable data actually suggests. He maintains the practical guidance sections of this resource, translating research findings into workflow-level recommendations that content teams can actually use.

Priya Nair

AI Tools Editor and Evaluator

Priya's background is in editorial quality assurance. When AI writing tools began proliferating, she became one of the few people systematically evaluating their output against real quality standards rather than just testing feature sets. She's responsible for the tool evaluation and responsible use sections of this resource, and she brings a healthy skepticism to claims made by both AI advocates and AI critics.