Technical SEO and Generative Search Readiness: A Structured Commercial R&D Audit Model

This technical report presents a structured commercial R&D audit model for evaluating the readiness of websites and digital information systems for both traditional search engines and generative AI-assisted search environments. The framework integrates technical SEO, crawlability and indexability, canonical architecture, information architecture, source credibility, entity and authorship signals, structured data, content freshness, discovery mechanisms, and measurement governance into a unified assessment model.

The report proposes a 100-point audit framework designed to separate technical accessibility from information quality, source traceability, entity consistency, and generative-search readiness. It emphasizes that visibility in AI-assisted search systems should not be treated as a standalone optimization discipline, but as an extension of robust technical SEO, structured information architecture, verifiable sourcing, and measurable digital-governance practices.

The methodology is intended for commercial R&D, technical auditing, digital strategy, and applied search research. It provides a repeatable framework that organizations can use to identify structural weaknesses, prioritize technical improvements, and evaluate how well their digital assets are prepared for evolving search and generative-answer environments.

The report is authored by Emre Kazanır as an independent commercial R&D technical publication affiliated with Ozem Dijital Pazarlama Ltd. Şti. (Ozemmedya), Türkiye. It is not presented as a peer-reviewed journal article.

DOI: 10.5281/zenodo.21855951