AI ClientsKedaiTools / Skills / Context
Enterprise control plane for AI integrations
Enterprise AI needs a control plane.
AI clients are moving into tools, skills, context, and internal systems. Kedai is focused on the control layer between them.
Thesis
AI integrations need enterprise control.
The next phase of enterprise AI is not just chat. It is integration: assistants using tools, following skills, reading context, and acting across systems.
That needs a control plane: one place to reason about identity, policy, capability, and audit across AI-driven work.
Approach
Kedai is focused on that control layer.
A layer between AI clients and enterprise integrations: MCP tools, reusable skills, internal context, and the policies around them.
The work is early. The direction is clear: enterprise AI will need control before it becomes infrastructure.
Surface
Tools, skills, policy, audit.
Kedai is focused on the control plane around the systems AI clients connect to and the instructions they inherit.
Tools
AI clients are gaining access to internal systems through MCP and other integration paths.
Skills
Reusable instructions and workflows need ownership, approval, and distribution.
Policy
Identity, role, context, and risk need to shape what AI can do.
Audit
AI-mediated work needs a record that does not become another sensitive data store.
Design
Built near the integration path.
Kedai is being designed around identity, policy, tool access, skill distribution, and audit with minimal unnecessary data retention.
Teams
For teams responsible for AI inside the enterprise.
Platform, security, and AI infrastructure teams will need clear control over how AI connects to internal systems.
- Platform engineering
- Security engineering
- AI infrastructure
- Enterprise founders
Contact us.
For questions or conversations, email hello@kedai.dev.