Answers for the way
you use AI today.

Cortega connects data protection, agent security, response verification, model routing, budgets, and operational signals. AI Border Gateway governs application and agent traffic; Endpoint Guard covers employee computers; RedTeam tests controls; AI Verifier checks responses.

Cortega supports deployment on your own infrastructure and a managed offering. Choose the deployment and model destinations that fit your data handling and operational requirements. Compare deployment options.

Governance requires inspecting selected traffic, and observability and Cortega Agents can use recorded traffic. Where that data is processed and retained depends on your deployment, logging configuration, enabled agents, and model destinations. Self-hosting gives you control over the infrastructure; it does not mean no data is processed or stored.

Use AI Border Gateway for application and agent traffic and Endpoint Guard for employee devices. Integration depends on the client and deployment: route supported calls through the gateway or configure endpoint protection for the relevant tools. Read the integration guides.

Yes. Manage and load balance these model types together, with routing rules for workload needs and policy. This helps regulated teams keep sensitive work on approved infrastructure while retaining access to other eligible models.

Endpoint Guard protects employee AI use on computers. AI Border Gateway applies guardrails to AI messages and MCP data, including sensitive-information redaction according to policy. Explore AI Data Protection.

AI Firewall is the solution powered by Cortega Guardrails. It governs requests, responses, tool access, and sensitive actions, including risks from prompt injection, social engineering, fraud, and data exfiltration. Explore Cortega Guardrails.

Cortega Guardrails check access to MCP servers and individual tools. An agent’s ability to discover a tool does not by itself authorize that agent to use it. Try the Agent poisoning scenario.

Yes. Alongside server and tool authorization, guardrails support deeper application-specific inspection. For vCon MCP records, this includes user consent and law enforcement detection. Explore the vCon Arena.

Guardrails can raise the data exfiltration threat level, increasing the caution Cortega exercises across the interaction. Sensitive data can also be redacted from AI messages and MCP data according to policy.

Cortega supplies policy controls, access management, observability, and audit evidence that can support compliance work. Compliance also depends on your policies, deployment, operating procedures, and applicable requirements; a product deployment alone is not a certification.

No. AI Verifier is not an MCP server and does not require one. Its primary workflow needs no plugins, file uploads, or files and context to be preloaded.

AI Verifier works across AI agents and models through Cortega’s governed traffic, including desktop assistants such as Claude and ChatGPT and network agents such as internal chatbots.

It checks policy violations and legal citations and improves factual relevancy and response accuracy using available contextual information. Explore AI Verifier.

No external subscription is required for the legal citation verification capability described on the AI Verifier page. The check operates inline in the governed workflow.

The product page describes legal citation accuracy of up to 100%, factual relevancy improvements of 20–25% without pretraining, and contextual accuracy improvements of 15–25%, depending on the model. Results depend on the task, model, and available context; these figures are not a guarantee for every response.

Routing can consider least cost, the meaning of the request, latency, workload capabilities, and policy. Text, audio, video, file uploads, and coding can use different eligible models. Explore AI Routing.

Cortega handles provider rate limits and server errors automatically using retry and failover to eligible alternatives. Availability still depends on the approved models and providers in your configuration.

Use monitoring, model studies, and evaluation to assess alternatives before switching. Coordinate models within workflows and retain approved fallback choices, while keeping model access consistent for applications.

Centralized key management keeps provider credentials under shared control across the AI infrastructure, instead of distributing raw provider keys to every agent and application.

The published gateway benchmark reports 52,000 requests per second on one 8 vCPU instance. Actual throughput and latency depend on traffic, providers, deployment, and enabled inspection and verification. Review the performance data and test configuration.

Yes. Set allowances for individual agents and groups of agents, and coordinate central shared budgets per provider.

As a budget runs low, model cost or usage can be reduced according to policy. Essential work can continue where the remaining allowance and approved model choices permit, rather than treating every budget threshold identically.

Yes. Restrict eligible models by agent or department and connect model selection to budgets and department policies. Explore Budget Management.

Cortega Agents analyze opted-in recorded traffic for sentiment, brand and policy compliance, conversation quality, topic and intent, cost efficiency, and agent behavior. Aegis watches for traffic anomalies. Explore AI Signals.

No. Most agents are off by default and require explicit source selection. Enable agents independently and choose AI Border Gateway traffic, specific applications, or Endpoint Guard traffic. Compliance analysis requires a rubric.

Workload Analyzer classifies exchanges. Paired with Model Intelligence, that classification informs recommendations for actual traffic, including a named successor when a model is deprecated.

RedTeam evaluates the behavior of models and active guardrails using safety tests. Use its results to review policy or model changes and compare outcomes. Explore RedTeam.

DLP and enterprise subscriptions address parts of data handling and provider use. Cortega adds controls across agent actions, MCP tools, response verification, model routing, shared budgets, and operational signals. Which controls overlap depends on your existing setup.

Routing is one part of governance. Evaluate whether your current setup also covers employee devices, application-specific tool inspection, response verification, agent budgets, and conversation-level signals. See why teams choose Cortega.

Start with approved models and tools, define data and action policies, set budgets, and evaluate representative workflows. Use recorded outcomes and AI Signals to refine the deployment. Explore AI Production Agents.

Foundation provides AI Border Gateway without a time limit. Check the current pricing page for the capabilities, usage allowances, and support included in each tier. View pricing.

Follow the installation path for your deployment and configure model connections, identities, policies, and traffic sources. Setup time depends on your environment and integrations. Read the installation guide.

Start with the workflows you need to govern: employee devices, production applications, tool access, response quality, or model operations. Review the product pages and current pricing, or request a demo for your environment.