Cortega sits in the path of AI requests, responses, and tool calls. It gives enterprises one place to observe AI use, enforce policy, protect data, govern MCP tools, route models, control cost, and keep audit evidence on infrastructure they control.
Most teams begin with a simple need: one endpoint for models, better routing, and visibility into cost. That is useful. The harder enterprise problem starts when AI touches regulated data, customer workflows, agents, MCP servers, internal tools, and employees using AI from many places.
Control model calls, retrieval, tool access, approvals, and sensitive data movement before an agent action completes.
Block, redact, route, require approval, or record decisions based on identity, data category, model, provider, tool, department, and intent.
Bring MCP servers, tool definitions, permissions, and tool calls into the same policy and evidence model as LLM requests.
Use Cortega Edge to extend governance to browsers, endpoints, assistants, and shadow AI paths across the enterprise.
| Need | How Cortega answers |
|---|---|
| Governance without sending traffic to a vendor cloud | Deploy on your infrastructure. Cortega governance does not need to see your data. |
| Policy before the call | Enforce before requests, responses, or tool calls proceed instead of only logging after the fact. |
| Enterprise operations | Use SSO, RBAC, budgets, audit evidence, standards-oriented telemetry, central management, and distributed gateways. |
| Model and provider flexibility | Use commercial providers, private models, and local LLMs as first-class choices. |
| Security and compliance posture | Model your intent, detect data and tool risk, record decisions, and improve posture over time. |
| Business visibility | See what teams are doing with AI, what they are worried about, where quality changes, and where strategy differs from execution. |
Govern AI applications, model gateways, agents, retrieval paths, and MCP toolchains used by products and internal services.
Extend controls to employee AI usage across browsers, endpoints, assistants, shadow AI tools, and internal agent workflows.
We published our first measured performance note for one Cortega gateway instance. It shows the setup, the raw results, and what one gateway carried before larger scale-out tests.
Tell us what AI traffic you need to govern. We'll help map the first useful control point without asking you to redesign your AI stack.
Request a conversation