The agent-native Growth OS that turns your AI agent into a disciplined growth hacker
Stage
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Founded
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Based in
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US
Pricing model
Contact for pricing
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Pricing
Pro plan for Thundercat-managed deployment includes ARIA, persistent GTM memory, standard skills and flows, supported tools and channels, managed infrastructure and onboarding. Enterprise plan includes everything in Pro plus private or on-premises deployment, model and data controls, retention optio
Free: 1 member, 1 project, unlimited free contributors and viewers. Solo: $19.90/month for 1 member with unlimited projects. Team: $39/month for up to 3 members, then $10/month per extra member, with unlimited projects and private projects.
Key features
Persistent GTM memory across tasks and teammates
Market signal detection and account qualification
Opportunity prioritization and next action recommendations
Customer health tracking and renewal risk assessment
Integrations with CRM, messaging and company documents
Support for up to 10 team members collaboration
Specialized GTM flows for marketing, sales and customer success
Forward deployment within 3 working days
Works across HubSpot, Salesforce, Slack and Notion
Complete experiment backlog with candidate ideas
Status tracking from idea to outcome
Shared experiment learnings across team and agents
ICE, PIE, RICE scoring frameworks with voting
Enforced experiment stages with mandatory results and learnings
MCP server for AI agent read and write access
Skills that teach agents the methodology
Plugin for Claude Code and Codex
Web app for team review and collaboration
OAuth security with granular permissions
What makes it different
Stateful agent that retains company knowledge and doesn't reset between workflows
Encodes your business judgment and qualification standards into how ARIA works
Carries company context across marketing, sales and customer success as one system
Forward deployed by Thundercat team within the business
Works alongside existing tools without replacing them
Agents cannot bypass the method: tests cannot close without measured results and written learnings
Enforced workflow prevents confident guesses and invented wins
Shared memory ensures experiments are never repeated and evidence is preserved
Agents read and write to the same record as teams under identical rules
Thundercat
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