Published September 13, 2025 | Version 1.0
Dataset Open

From Snapshots to Standards: Measuring AI Visibility with the AIVO Standard™ and PSOS™ Auditing

Authors/Creators

Description

From Snapshots to Standards: Measuring AI Visibility with the AIVO Standard™ and PSOS™ Auditing addresses the emerging challenge of measuring brand presence in AI assistants such as ChatGPT, Gemini, Claude, and Perplexity.

Current visibility tools (e.g., Profound, Evertune, PEEC AI, Semrush, AthenaHQ) rely largely on snapshots — one-off screenshots or share-of-voice counts — which provide directional signals but lack reproducibility, sustainability, and audit integrity. This white paper introduces the AIVO Standard™, a proposed framework for AI Visibility Optimization (AIVO), and its associated Prompt-Space Occupancy Score (PSOS™) auditing system.

PSOS provides a structured, repeatable methodology:

  • systematic prompt set design across awareness, consideration, and decision stages;

  • multi-LLM execution with time-stamping, hashing, and optional notarization;

  • top-tier scoring rules with sentiment filters;

  • reproducible benchmarks suitable for governance and competitive comparison.

The paper contrasts snapshot and PSOS approaches using stylized case studies in banking, SaaS, and consumer goods. Appendices include a sample dataset, workflow notes, cost estimates, an output record example, and discussion of limitations (e.g., prompt bias, model drift, resource intensity).

The contribution is twofold:

  1. To demonstrate why snapshots are insufficient for governance, investment, or strategy.

  2. To propose PSOS as a repeatable, audit-grade benchmark that could form the basis of future standardization efforts (e.g., ISO, CEN, IEEE).

This work will be of interest to scholars, practitioners, and policymakers in AI governance, marketing analytics, and information retrieval.

Files

AIVO_Standard_PSOS_WhitePaper_Final.pdf

Files (248.6 kB)

Name Size Download all
md5:7a89e5a153e270d7427b31deb9d873af
248.6 kB Preview Download