Perception Categorization
Differentiate between passing brand mentions and genuine endorsements, categorizing AI responses by sentiment polarity and buying sentiment.
Stage 01 • Measure
Understand How AI Engines Perceive and Describe Your Brand
Analyzes how AI engines talk about your brand — positive, neutral, or negative — not just whether they mention you. Detect subtle bias and pinpoint the exact sources shaping generative perceptions.
Tone Analysis
Live Diagnostic View
Sentiment Breakdown Across Queries
Overall Engine Perception 100% Analyzed Prompts
Positive Tone 68%
Neutral Tone 28%
Negative Tone 4%
Positive Perception Sample
"Frequently highlighted as "best-in-class integration speed and reliable API uptime.""
Neutral Context Sample
"Listed neutrally alongside 3 enterprise vendors in general feature matrices."
ReachHub Diagnostic Engine • Real-time capability preview
Core Advantages
Engineered for Authoritative Visibility
Source Attribution
Trace sentiment origins back to specific third-party reviews, press coverage, or outdated articles that generative models use as training context.
Sentiment Trajectory
Track sentiment evolution over time to measure how product releases, PR campaigns, and content updates shift LLM perception.
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