Why there is no universal standard
Unlike domain authority (a proprietary 0–100 score published by Moz and Ahrefs) or keyword ranking position (a 1–10 scale), there is no published, standardized AI visibility score. AI engine operators do not release the factors or weights that determine recommendation probability.
This means your AI visibility score is necessarily self-defined — a structured measurement you create and track over time.
A practical scoring framework
Here is a framework used to build meaningful, comparable visibility scores:
Step 1: Build a query set
Create a query set of 15–20 queries that represent how potential clients find businesses like yours. Include:
- 5 branded queries ("What does [Your Business Name] do?")
- 5 category queries ("Best [service type] in [city]")
- 5 problem queries ("Who should I call if [specific problem]?")
- 5 comparison queries ("Should I use [Your Business Name] or [Competitor]?")
Step 2: Test across four engines
Run each query in ChatGPT, Perplexity, Google AI Overviews, and Gemini while logged out. Record whether your business appears in the response — as the primary recommendation, as a named alternative, or not at all.
Step 3: Score by query type and engine
Award points:
- Primary recommendation: 3 points
- Named as a credible alternative: 1 point
- Not mentioned: 0 points
Maximum score: 15 queries × 4 engines × 3 points = 180 points.
Interpreting your score
- 0–30% of maximum: Entity foundation needs work. Focus on schema, GBP, and About page.
- 30–60% of maximum: Foundation is established; content and reputation authority need investment.
- 60–80% of maximum: Strong performance. Category query gaps remain — focus on content targeting those queries.
- 80%+ of maximum: Category authority achieved. Maintenance and competitor monitoring are the priority.
