Accountable AI for autonomous work that answers for itself
The agent-native Growth OS that turns your AI agent into a disciplined growth hacker
Stage
Startup
Not given
Founded
2026
Not given
Based in
DE
US
Pricing model
Paid
Not given
Pricing
Founder $1,000/month, Team (up to 10 people) $3,000/month, Investor firm $3,000/month, Company from $10,000/month. Early members receive launch prices set on individual calls. Flat plans with unlimited jobs, no per-seat fees, and month-to-month billing.
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
Autonomous job planning and execution with parallel specialists
Independent verification check before results reach user
Integrates with existing apps and accounts without API requirements
Private per-customer workspace with encryption in transit and at rest
Corrections become rules for future runs
Full audit trail with source links for all findings
Month-to-month billing with no lock-in contract
Unlimited jobs on flat monthly pricing
Results within the hour after setup
Draft approval workflow for outbound communications
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
Answers for the work rather than just running tasks autonomously
No setup required; company handles configuration and ongoing management
Separate verification step ensures quality before delivery
Works across 35 AI providers through user's own API keys
Customer playbooks never shared with competitors
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
Hypotize