Runtime Decision Enforcement for AI Systems
Trampolyne AI applies enforcement at the moment an AI system decides to act - inline, before execution.
Policy-as-Code for Runtime AI Enforcement
Enforcement logic is treated as code - versioned, attributable, and audit-ready by default.
Executable policy
Policies compile directly into runtime logic
Versioned changes
Who changed what, when, and why
Audit-ready
Exportable enforcement evidence
Inline by design
Humans, services, and internal applications issuing AI requests
Runtime enforcement
LLMs, agents, tools, and downstream APIs
Runtime decision pipeline
Every request and response is evaluated before execution.
User, agent, data, tool, and session context
Model safety signals combined with organization-defined policy
Deviation from expected usage patterns
Allow, restrict, modify, or block
Simple integration
No SDK sprawl. No model rewrites. Trampolyne AI integrates as an API gateway or proxy layer. This allows teams to secure AI systems without refactoring models or workflows.
→ Trampolyne AI policy enforcement
→ LLM / agent execution
→ Response enforcement