AIV Methodology
The AI Visibility Index (AVI) is a 0–100 composite score that measures how visible a brand is to AI search engines. It answers one question: when a customer asks an AI for a recommendation, does your brand appear? This page is the full public scoring model — the same one every Visora audit runs on.
The 7 metrics
- Recall (25%) — the fraction of AI responses that name the brand
- Breadth (15%) — average recall across each engine (per-engine consistency)
- Depth (15%) — the richness of the mention (capped at 500 characters)
- Keyword Reach (15%) — the fraction of distinct queries where the brand appears
- Convergence (10%) — how consistently the engines describe the brand (NLI + Epanechnikov kernel; needs ≥2 mentions)
- Factuality (10%) — the fraction of canonical facts the AI gets right
- Rank (10%) — position vs competitors (1st = 1.0, decaying by 0.8 per position)
The formula
AIV = 100 × Σ(weight × metric) ÷ Σ(weight for non-null metrics). Optional metrics (Convergence, Factuality) return None when data is insufficient — their weight redistributes proportionally to the metrics present.
Query methodology
Scans use generic discovery queries — the kind a customer asks without knowing the brand exists: "Best [Category] in [City]", "Recommend a good [Category] in [City]", "Leading [Category] providers in [City]". Brand-centric queries are never used: they guarantee a mention, which measures recall of a question, not visibility.
Engines and score bands
Eight engines are scanned: ChatGPT, Google AI, Gemini, Microsoft Copilot, Perplexity, Doubao, DeepSeek, and Qwen. Score bands: 0–30 Invisible · 31–55 Emerging · 56–75 Established · 76–100 Authoritative.
Limitations
- The quick audit is LLM-estimated, not retrieval-grounded
- Convergence and Factuality need at least 2 mentions to compute
- English-language query bias
- Scores are time snapshots — AI models update continuously
Citation
Visora. (2026). The AI Visibility Index: A Composite Metric for Brand Visibility in AI Search. Methodology v1.0. Available at https://visoraco.com/research/methodology
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