systemprompt MCP
Self-hosted AI governance platform — control, audit, and secure every AI tool call and model inference from a single binary.
| What is it | Self-hosted AI governance platform — control, audit, and secure every AI tool call and model inference from a single binary. |
|---|---|
| Pricing | Unknown |
| Platform | Web Application |
| API | Yes |
| Best for | enforcing access controls and data security for internal AI tools, generating audit trails for AI usage to meet regulatory requirements |
| Domain registered | 2024 |
Data updated Aug. 1, 2026
What does systemprompt MCP do?
systemprompt.io is a self-hosted platform that gives organizations complete control over how they use AI. It acts as a central gateway and governance layer for all AI activity. You run a single binary on your own infrastructure, and it manages everything: it routes requests to any AI model provider (like Claude, OpenAI, or your own self-hosted models), checks and controls every tool call an AI makes, and logs every action for auditing. The core idea is to bring AI execution and governance into one system you own, so no data has to leave your network.
It works by sitting between your users (or applications like Claude Cowork) and the AI models. Every time an AI tries to use a tool—like accessing a database or an API—the request passes through systemprompt.io's checks in milliseconds. These checks include verifying user permissions, scanning for secrets, applying blocklists, and enforcing rate limits. If a call is denied, it never reaches the model or the external service. All of this activity is logged in a structured audit trail in your own database, linking each action to a specific user and session.
This tool is built for organizations with serious security, compliance, and cost-control needs. It benefits companies in regulated industries (finance, healthcare) that must prove AI actions for audits, tech teams that want to use multiple AI models without vendor lock-in, and any business that needs to keep sensitive data completely in-house. Use cases include securely deploying AI assistants like Claude Cowork across a company, governing internal AI agent fleets, and creating a compliant, auditable AI infrastructure that meets standards like SOC 2 or HIPAA.
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