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
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Pricing
Free: 5 successful runs per UTC day after email verification. Starter: $9.90/month for 30M tokens/month. Builder: $19.90/month for 80M tokens/month. Pro: $39.90/month for 200M tokens/month. Scale: $79.90/month for 500M tokens/month. Tokens refresh monthly and do not roll over. One-time token packs a
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
Choice decision type: select from supplied options
Score decision type: assign level on defined scale
Noul decision type: yes/no judgment against criterion
Decision flows: chain questions on canvas with branching
Batch processing for CSV, TXT, JSONL files up to 1,000 rows
Shared account balance across console, batch and API
Export results as CSV or JSONL
Failed requests are refunded
Probabilities included with all answers
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
One API and console for playground, flows, batch and API access
Only questions on the executed path in flows are billed
Built on TypeSafe AI's Jev structured decision model
Test and measure decisions on your own data before production
Custom decision criteria defined by your own labels and rules
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
JevModel
Hypotize