AIVO Optimize v1.0: Self-Serve Decision-Stage AI Visibility Diagnostic Platform

This paper documents the architecture, methodology, and commercial implementation of AIVO Optimize v1.0, a self-serve platform for measuring brand performance at the AI decision stage. AIVO Optimize operationalises the Conversational Decision Audit (CODA) scoring engine, first formalised in WP-2026-01, into a structured diagnostic product accessible to brand teams and agency practitioners without managed service engagement. The platform runs four-turn structured buying sequences across five AI systems (ChatGPT, Perplexity, Gemini, Grok, and Claude) and returns a CODA score segmented by platform and probe type. The diagnostic covers three probe categories: undirected journey probes, directed brand probes, and competitive displacement probes. The primary commercial output is a T4 win rate: the proportion of complete four-turn buying sequences in which the brand receives the final AI recommendation. AIVO Optimize addresses a structural gap in existing AI brand measurement infrastructure. Commercial platforms in the AI visibility category measure citation frequency, sentiment, and share of voice at early funnel stages. None segments measurement by purchase stage or reports a T4 decision metric. AIVO Optimize's CODA score is, to the authors' knowledge, the only commercially available metric that measures AI recommendation performance at the point of purchase influence. This paper documents the platform's probe methodology, scoring architecture, five-platform coverage rationale, output structure, pricing tier model, and relationship to the AIVO Standard parent methodology. It is intended to serve as a citable technical reference for practitioners, researchers, and standards bodies engaged with AI brand measurement.