Audit & Measure Brand Visibility in Generative AI Responses
When prospective buyers ask ChatGPT, Perplexity, or Gemini about your industry services, is your brand cited and recommended? Empirical evaluation backed by Wilson 95% confidence intervals and Hamzad Inten optimization playbooks.
Real-Time AI Visibility & Share of Model Audit
Why INTEN AI Visibility Measurement is Different
Unlike legacy SEO that only monitors Google's 10 blue links, this platform quantifies brand presence within synthesized conversational answers.
Multi-Engine Grounding
Measures live grounding across Perplexity Sonar, ChatGPT Search, and Google Gemini with multi-perspective prompt variations.
Wilson 95% Confidence Intervals
Eliminates point-estimate hallucinations by calculating rigorous mathematical confidence bounds [Lower - Upper Bound] for recommendation rates.
Hamzad AI Action Blueprints
Converts citation gaps into concrete schema injection blueprints, entity reconciliation graphs, and high-converting Q&A clusters.
Everything You Need to Know About AI Visibility
How does this tool test AI engines without burning paid credits?
Initial technical signals (AI crawlers, robots.txt directives, and JSON-LD schemas) run completely free with zero token usage. When testing live buyer questions, 3 full deep audits are provided post-login, allowing you to inspect live answer citations and competitor appearances.
What happens if I notice a discrepancy with my manual chat results?
If you manually saw your brand recommended in ChatGPT or Perplexity, click the "Report AI Result Discrepancy" button. Your prompt and observation are submitted directly to Inten AI to audit model parameters and expand entity recognition.
How should I enter keywords in the form?
Please separate each product or service keyword with an English comma (,) so the query simulation model can cleanly construct 20 distinct buyer evaluation scenarios.