Platform Features
A complete toolkit for finding and fixing what AI gets wrong about your brand across ChatGPT, Gemini, and Perplexity.
AI Visibility Score
A transparent, weighted score (35% mention rate + 25% recommendation rate + 20% citation rate + 10% position + 10% sentiment) that gives you a single, explainable number for how accurately AI represents your brand.
Share of Voice
See what percentage of AI answers mention your brand versus tracked competitors. Track shifts over time and identify which competitors are gaining ground.
Multi-Provider Analysis
Run identical prompts against OpenAI web-search, Gemini grounded, and Perplexity web-grounded models. Find what each provider gets wrong about your brand in one unified view.
Mention vs. Recommendation
We distinguish between a brand being mentioned and being actively recommended. Recommendation requires language indicating suitability, preference, or direct proposal as a solution.
Citation Analysis
Track which sources AI systems cite when discussing your brand. Identify official-domain citations, third-party authority sources, and domains that support competitors.
Stability Scoring
AI answers can vary between identical runs. We repeat tests and calculate a stability score with confidence levels: low (1 run), medium (2-3 runs), high (4+ runs).
Action Center
Get evidence-backed actions to fix what AI gets wrong, with impact/effort ratings and priority scores. Each action links to the specific prompts and responses where AI got your brand wrong.
Client-Ready Reports
Generate weekly, monthly, or campaign reports with summaries, key wins/losses, competitor movements, and recommended actions. White-label for agency clients.
Multi-Country & Language
Test prompts across different countries and languages. Understand how AI accuracy varies by geography and locale.
Team & Roles
Workspace Owner, Admin, Analyst, and Client Viewer roles. Agency client viewers see only assigned brands. Full multi-tenant isolation.
Transparent Methodology
Every score is explainable. Full disclosure of formulas, data collection methods, and limitations. Provider labels are accurate—never claiming consumer-app parity.
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