The Context Graph for
AI Governance.
Bridging the gap between Human Intent and Agent Action.
From “Big Data” to
“Deep Context”.
In the last decade, the challenge was storing Data (what happened).
In the AI era, the challenge is capturing Context (why it happened).
“The last generation of software captured the result. This generation must capture the reasoning.”
When an Engineer (or Agent) commits code, they change the business reality. But the “Translation Gap” means context is often thrown away at commit.
“I added an AI model to rank applicants faster.”
“We just moved into a high-stakes automated decision use case—so risk, transparency, and governance requirements kick in immediately, even before we debate what data the model touched.”
Risk is discovered in production—during an incident or audit—when it is too expensive to fix.
Why ChooChoo?
Three converging trends make context governance essential: the rise of AI agents, spec-driven development, and regulatory pressure.
Context Governance
Prove compliance with cryptographic audit trails and decision traces for regulatory requirements.
Agent Rails
Keep AI agents safe and governable with explicit boundaries and institutional memory.
Standards Native
Native support for ODPS, ODCS, Arazzo, and other open standards for data and AI governance.
Documentation
Explore comprehensive guides, architecture details, and API references.
Getting Started
Installation, quickstart, and initial setup
Core Concepts
Understand the fundamental ideas behind ChooChoo
Architecture
Deep dive into system design and components
Developer Guide
Build with ChooChoo and extend its capabilities
Enterprise
Governance, compliance, and enterprise features
References
API reference and specifications
Ready to get started?
Join the movement toward truly governable AI. Start building with ChooChoo today.