How QueryArc measures brand visibility across 5 AI assistants (ChatGPT, Claude, Gemini, Perplexity, Grok), reduces variance with repeat runs, and keeps results traceable back to raw outputs.
Each audit uses a structured prompt pack designed around real buyer-intent queries. Prompts are clearly defined, versioned, stored, and reused for reruns. This prevents question drift and allows comparable measurement over time.
prompt_pack: v1
total_prompts: 10
non_brand: 8
brand_name: 2
The full prompt list is included in the report.
Audits are run across multiple AI engines. Results are shown separately for each engine. We do not blend or average engine results without disclosure.
Runs per prompt: 3
Reported separately
Runs per prompt: 3
Reported separately
Runs per prompt: 3
Reported separately
Runs per prompt: 3
Reported separately
Runs per prompt: 3
Reported separately
Engine-level reporting is never overridden by aggregate views.
Each prompt is executed three times per engine. AI models are probabilistic systems and may produce slightly different responses on repeated runs. Running multiple times reveals stable patterns.
Only successfully completed runs are included in denominators.
Every AI response is evaluated using fixed detection rules. No human manually adjusts scores.
Percentages and rates are calculated using a single formula. No estimates, sampling adjustments, or manual overrides are applied.
Matching runs ÷ Total valid runsOnly successfully completed runs are included.
Reports include multiple layers of analysis. This shows exactly where outcomes differ across prompts and models.
Reports include representative excerpts from AI responses. Full raw outputs and scored datasets are preserved in technical appendices. This allows complete traceability from summary metrics back to original model responses.
Nothing is manually interpreted before scoring.
After quantitative scoring is complete, results are interpreted using a structured diagnosis framework. Diagnosis does not modify mention rates, endorsement rates, ranking metrics, or comparison outcomes. All numerical values remain rule-based.
Rule-based, deterministic
Structured interpretation
Diagnosis does not modify numerical results.
Low non-brand mention → Category association gap → Category landing page
Diagnosis explains patterns. It does not alter scoring outputs.
Each audit stores its run configuration. When reruns are performed, the same configuration is used to maintain comparability.
prompt_pack: v1
models: [ChatGPT, Claude, Gemini, Grok, Perplexity]
timestamp: 2025-01-15T09:30:00Z
settings: stored
Reruns use the same stored configuration.
AI model behavior can vary between runs and can evolve over time due to provider updates. No single response determines a conclusion.
AI models are probabilistic systems.
In some views, results may be shown "together." This means all valid runs across engines are combined into a single aggregated dataset.
Engine-level reporting remains available.
AI systems evolve over time. Outcomes may change as models are updated.
QueryArc uses:
This produces auditable, reproducible, and interpretable AI visibility metrics.
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