ABOUT · WHY CORTEGA
Control data access, agent permissions, model output and cost.
Protect the data they share. Set boundaries on what agents can do. Check the answers they rely on. Cortega connects those controls with the models, tools, and infrastructure you choose.
AI WORKFLOWS
A developer connects an agent to company tools. An employee pastes a customer record into an assistant. A lawyer asks AI to interpret a contract.
Check data destinations, authorized actions, answer quality and workflow costs.
Cortega applies these controls to AI requests and tool calls.
CONNECTED CONTROLS. PRACTICAL OUTCOMES.
From the employee’s device to the agent’s next tool call, put the right controls where the work happens.
Inspect governed prompts, responses, and tool data for sensitive information. Mask or block it according to policy, and route sensitive workloads to approved destinations.
Explore data and agent securityAuthorize the models, MCP servers, and individual tools an agent can access. Connect identity, guardrails, and audit evidence so you can understand what was allowed and why.
Explore AI Border GatewayCheck citations and source support, flag unsupported claims, and improve relevance. In legal work, bring those checks into matter workflows and the review of AI-generated drafts.
Explore Cortega Legal AIBUILT AROUND THE WAY PEOPLE WORK
AI adoption happens in production applications, coding assistants, browsers, and desktop tools. Cortega gives you connected ways to govern that work.
AI Border Gateway brings routing, guardrails, access policies, budgets, and observability into the traffic you send through it.
Govern production agentsEndpoint Guard discovers supported AI use on macOS and runs local data checks on configured applications. Pair it with the gateway for shared visibility and optional analysis.
Bring employee AI into viewConfigure supported coding assistants to use your gateway, with approved models, centralized provider credentials, and controls on cost and data.
Explore coding agent governance
YOUR MODELS. YOUR INFRASTRUCTURE. YOUR RULES.
Your next model should be a choice you can evaluate. Your next deployment should fit the boundaries your business needs.
Use local, open-weight, and hosted models through approved routes. Choose self-hosted or managed deployment. Apply budgets and route by cost, capability, and latency, with configured failover when a provider has trouble.
Private AI depends on the whole workflow: model endpoints, verification, and analysis services must be configured for the boundary you require.
PUBLISHED EVIDENCE
Security and quality change with every model, prompt, and tool. Make evaluation part of how you build and operate AI.
Cortega RedTeam turns research and public datasets into runnable benchmark suites. Test models, adaptive attacks, poisoned MCP results, and protected configurations in your own environment.
Inspect responses and judgments, compare with your baseline, and retest after changes. Your team chooses the target models, LLM judges, and schedule.
Build your benchmarking capabilityBenchmark execution is available in the on-premises solution. Automated scores inform decisions alongside professional review.
52,000
100% success in the published fixed-rate load test. Review the setup and results, then measure your own workloads with the controls you plan to enable.
Inspect the performance data Explore public benchmark resultsWHY WE BUILD CORTEGA
AI requests can contain sensitive data, unsupported claims and instructions to call tools.
Our founders bring experience in enterprise security, critical infrastructure, and healthcare AI. We bring that discipline to the practical work of making AI safer, more accountable, and more useful.
Meet the team and our mission Explore Cortega Research LabsDEPLOYMENT REQUIREMENTS
Review deployment, data access, permissions and output checks with our team.