The AI Visibility Index (AVI): A Framework for Measuring Data Discoverability Across AI Systems

Executive Summary

Large Language Models (LLMs) and generative search now mediate how information, products, and brands are discovered. The AI Visibility Index (AVI) quantifies how effectively a brand’s data, products, and knowledge are discovered, retrieved, and referenced by AI systems such as ChatGPT, Perplexity, Google SGE, Bing Copilot, and Amazon Rufus.

AVI expresses this as a comparable 0–100 score, capturing visibility across platforms and breaking it down into sub-indices for inclusion, ranking, knowledge representation, traffic outcomes, and brand perception. This article defines the AVI, details its calculation, and outlines how to operationalize and maintain it as part of an ongoing visibility intelligence program.

1. Defining the Phenomenon

The objective is to quantify a brand’s data discoverability—how often and how prominently its structured data and knowledge appear within AI-generated responses and recommendations.

Definition:
AI Visibility is the rate and prominence at which a brand’s structured data and knowledge are retrieved and referenced by AI systems relative to peers and over time.

2. Inputs and Outputs

2.1 Inputs – Drivers of Discoverability

  1. Data Quality & Structure (Q)

    • Schema markup completeness (JSON-LD, RDFa)

    • Metadata accuracy (GTINs, titles, attributes)

    • Entity linking (Wikidata, Wikipedia, KGs)

    • Availability of structured datasets or APIs

  2. Content Semantics (S)

    • Topical relevance and entity density

    • Embedding similarity to model training data

    • Natural language clarity and contextual richness

  3. Source Authority & Citation Network (A)

    • Backlinks from authoritative domains

    • Mentions on trusted sources (Wikipedia, Reddit, PubMed, etc.)

    • Domain authority and E-E-A-T metrics

  4. Platform Integration (P)

    • Inclusion in AI knowledge sources (Bing, Google, Perplexity, etc.)

    • API accessibility and indexability

    • Crawl frequency and compliance with AI retrieval policies

  5. Engagement & Behavioral Data (E)

    • CTR, dwell time, saves, shares

    • Mentions in AI recommendations or chat responses

    • Sentiment and trust perception

  6. Technical & Governance Factors (T)

    • Data freshness and version control

    • Content licensing and AI training eligibility

    • Use of open metadata standards

2.2 Outputs – Observable Results

  1. AI Inclusion Metrics (I)

    • Inclusion frequency in AI responses

    • Citation rate of brand-owned sources

  2. Recommendation Visibility (R)

    • Share of voice in AI-powered search

    • Ranking prominence in recommendation systems

  3. Knowledge Graph Representation (K)

    • Number of recognized entities

    • Attribute completeness and linkage accuracy

  4. Traffic & Engagement (F)

    • AI-driven referral sessions

    • Conversions or actions attributed to AI visibility

  5. Brand Perception & Trust (B)

    • Sentiment polarity in AI summaries

    • Factuality and hallucination rate

    • Perceived authority in generative outputs

Francesca Tabor