AI assistant that automates desktop tasks by learning from your actions
The agent-native Growth OS that turns your AI agent into a disciplined growth hacker
Stage
Not given
Not given
Founded
Not given
Not given
Based in
US
US
Pricing model
Freemium
Not given
Pricing
Free: $0 per user with 10 included workflow runs and basic features. Plus: $20 per seat per month with expanded usage. Pro: $100 per seat per month with 5x more usage than Plus. Annual billing available at 20% discount. Enterprise plans available with custom pricing.
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
Learn tasks by demonstration - show once, replay automatically
Works with existing apps via screen automation without integrations
Voice command interface
Team collaboration with shared playbooks
Automatic workflow detection and updates when apps change
Scheduled task automation
Manual and automatic approval modes
Content generation with image and music creation
Knowledge base for team documentation and context
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
No integrations required - uses screen automation like a human would
Learns by watching - no code or complex setup needed
Automatically adapts when app interfaces change
Voice-controlled AI that understands context from your screen
Full team access to shared automations and knowledge base
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
Audimate
Hypotize