A Public Benefit Corporation

The agent serves the human.

Gehirn builds the infrastructure for AI agents that actually serve people — not the computer. Security, orchestration, and personal intelligence, unified in one stack.

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Product Stack

Infrastructure for agents that serve people

lobstercage

Security

Security scanner and live guard for the agent ecosystem. PII enforcement, content policy scanning, and CI integration for safe skill deployment.

KaizenOS

Channel

Your ambient personal OS. Themes, actions, and calendar integration that learns how you work — the agent that knows you.

Hammurabi

Orchestration

Mission control for your AI agents. Quest board, legion workers, and a commander interface for managing complex agent operations at scale.

gfinance

Application

AI-native finance for the operator who doesn't have a CFO. QuickBooks integration, automated bookkeeping, and intelligent financial insights.

g3lobster

Channel

Multi-agent service with named personas and layered memory. Reaches you through Google Chat, WhatsApp, and Telegram — channels you already live in.

How it fits together

  Channel        │  KaizenOS · g3lobster
  ─────────────────────────────────────
  Orchestration   │  Hammurabi
  ─────────────────────────────────────
  Security        │  lobstercage
  ─────────────────────────────────────
  Application     │  gfinance

2,800+

PMAI Community Members

Pioneering Minds AI — the fastest-growing AI practitioner community driving real-world agent adoption.

Pioneer Track

Operator-Led Innovation

Every product in the Gehirn stack was born from production use — built by operators, for operators.

Case Studies

Proven results across industries

Legal

AI Document Review for Mid-Size Law Firm

A 25-attorney firm spent 40+ hours per week on manual contract review and due diligence, creating bottlenecks that limited the number of active cases they could handle.

60%

Review time saved

Cases handled

< 2 wks

Time to deploy

Real Estate

Automated Market Analysis for NYC Brokerage

Agents at a Manhattan brokerage spent 3-4 hours per listing preparing Comparative Market Analyses manually, pulling comps from multiple sources and formatting reports by hand.

85%

Faster CMA

40%

More listings

2 hrs

Saved per CMA

View All Case Studies

Ready to build with us?

Whether you're exploring agent security, personal AI, or enterprise orchestration — we'd love to talk.

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