AI agents for procurement that work inside Microsoft 365 and Google Workspace
The agent-native Growth OS that turns your AI agent into a disciplined growth hacker
Stage
Not given
Not given
Founded
Not given
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Based in
DE
US
Pricing model
Paid
Not given
Pricing
Starter: €295/month for up to 20 sourcing events/year; Growth: €645/month for up to 50 sourcing events/year; Scale: €1,195/month for up to 100 sourcing events/year; Enterprise: custom pricing for 100+ events/year. All plans include unlimited users. Events beyond plan run at €249 each. Annual 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
Draft RFx documents with evaluation criteria and submission guidelines
Vendor research and shortlisting
Pricing and compliance matrix comparison
Negotiation preparation and leverage analysis
Works inside Microsoft 365 and Google Workspace
Structured workflow process
Company template support
Event rollover and flexible billing
Human review and control at every step
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
Operates inside existing tools without requiring adoption of new software or workflows
Structured, multi-phase workflow prevents steps from falling through the cracks
Category-specific AI knowledge designed for procurement rigor
Human-in-the-loop design with transparent, auditable recommendations
Pricing by sourcing volume, not seats, with unlimited users
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
Caddygo
Hypotize