AI Identity Diagnostic — Deterministic Interpretation of Whole-Site Entity Graph Evidence for Machine-Readable Business Identity Assessment

Engineering notes

Engineering notes will be added by the aipentium editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

The AI Identity Diagnostic is a deterministic interpretation methodology developed by Sydney Business Web for assessing whether completed whole-site entity graph evidence resolves into a coherent machine-readable business identity. It operates after a completed Schema Gorilla analysis and evaluates evidence including primary business identity, WebSite-to-business linkage, core Person-to-business linkage, identity consolidation, direct entity relationships and unresolved contextual entities. This public release documents the methodology, diagnostic state model, evidence boundaries, completion requirements, system architecture and an anonymised worked example. Version 0.4.5 introduces the explicit NOT ASSESSABLE state for identity-consolidation tests where the relevant entity class is absent. This prevents absence of business or Person identity evidence from being incorrectly represented as successful consolidation. The deposited material contains public technical architecture, design and methodology documentation. The production implementation, proprietary source code, internal rules, thresholds, authentication mechanisms, customer data and remediation procedures remain proprietary to Sydney Business Web. The AI Identity Diagnostic does not claim to guarantee ranking, recommendation, citation or selection by Google, ChatGPT, Gemini, Perplexity or other AI and search systems. Its purpose is to interpret whether genuine business evidence is presented in a structurally coherent machine-readable form. Technical authorship: Keith Rowley, Co-Owner and Lead Engineer, Sydney Business Web. BSc (Hons), MBA; former Professional Engineer (Pr.Eng.) and MSAIEE.

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