AI visibility platform that helps brands and agencies track and improve citations across ChatGPT, Gemini, Perplexity, and AI search engines
Discovery engine for unidentified data phenomena
Stage
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Founded
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Based in
AE
US
Pricing model
Freemium
Contact for pricing
Pricing
Basic: $29/month for 1,800 AI answers a month. Standard: $97/month for 6,500 AI answers a month. Growth: $249/month for 18,000 AI answers a month. Scale: $497/month for 36,000 AI answers a month. All plans include 7-day free trial.
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Key features
Citation tracking across ChatGPT, Gemini, Perplexity, Google AI Overviews and Google AI Mode
Content Opportunities identifies content gaps where competitors are cited
KIVA writing agent drafts AI-optimized content
Performance History compares visibility on any two dates
White-label client dashboards per client and country
Google Search Console integration
Unlimited projects, prompts and seats on all plans
Outreach to cited pages with verified contacts
Daily monitoring and movement tracking
REST API and WordPress integration
Support for seven data shapes including time series, events, networks, and spatial measurements
32 categories spanning domains from financial markets to genomics
Discovery engine for changing relationships and recurring behaviours
Reproducible execution with documented model configuration and runtime context
Confidential compute options including Intel TDX, AWS Nitro Enclaves and AMD SEV
Developer API and GitHub integration for agent-based exploration
Support for investigating evidence and testing explanations
What makes it different
Citation Score metric focuses on source citations AI actually pulls from, not just mentions
30 Day Gameplan delivers ranked actions to create, optimize and reach out
Daily tracking shows lift in weeks instead of quarters
No feature gating between plans from $29 to $497
C.I.T.A.T.I.O.N. framework guides optimization through eight steps
Separates phenomenon discovery from explanation, allowing rigorous investigation of root causes
Retains all outputs alongside original data for inspection and reproducibility
Works with existing data without requiring pre-identification of phenomena
Keeps execution accountable through documented model identity and configuration