Every score is reproducible. Here is exactly what we ask, who we ask, and how we calculate the result.
Model list last updated: October 12, 2026
A standard scan fires 25 prompts across five buyer-journey stages, each generated fresh for your brand and domain. The category mix reflects where buyers actually are in their decision process.
Buyers describe pain, not your name. If you're absent here, you're invisible at the top of the funnel.
"What tool helps a B2B SaaS team track churn?"
When buyers already know the category they need, your brand must appear as a credible option.
"What are the best AI-visibility platforms for marketing teams?"
Direct brand queries test whether AI actually knows what you do. That is the baseline of factual authority.
"Tell me about Acme Analytics and what they offer."
Buyers considering a switch ask for alternatives. Appearing here turns competitive pressure into pipeline.
"What are the best alternatives to Competitor X?"
Direct head-to-head questions are the highest-intent queries at the bottom of the funnel.
"How does Acme Analytics compare to Rival Y?"
| Category | Prompts | Share |
|---|---|---|
| Problem-led | 8 | 30% |
| Category-aware | 6 | 25% |
| Brand | 5 | 20% |
| Alternative-seeking | 4 | 15% |
| Comparison | 3 | 10% |
| Total | 25 | 100% |
Every scan produces these three components. Here is a static illustration using example data.
Visibility score
95% confidence interval from 4 runs × 25 prompts × 4 models
Per-provider breakdown
Score trend (6 scans)
+15 pts over 6 scans
Each model response produces one result. Results are scored individually, then averaged across all prompts and models to produce the final 0–100 score.
Per-response score
score = base × mention × sentiment × citation × position_decay
Final score
final = mean(per_response_scores), clamped to [0, 100]
Example: 3 prompts, 1 model
| Prompt | Mentioned | Rank | Sentiment | Score |
|---|---|---|---|---|
| Awareness #1 | yes | 1 | neutral | 100 × 1.0 × 1.0 × 1.00 = 100 |
| Evaluation #1 | yes | 3 | positive | 100 × 1.0 × 1.2 × 0.72 = 86 |
| Comparison #1 | no | n/a | n/a | 100 × 0.0 × n/a × n/a = 0 |
| Visibility score | mean(100, 86, 0) = 62 | |||
A full audit asks every model listed below. The free scan asks ChatGPT and Claude and checks Gemini separately with live search . Scores reflect real API responses. No caching, no mocking.
Current production models from OpenAI · Anthropic · Google · Perplexity
| Provider | Model IDs |
|---|---|
| Anthropic |
|
| Google (Gemini) |
|
| OpenAI |
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| Perplexity |
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Model IDs are read from the live routing configuration, not hand-maintained here. Updated when models rotate.