Enzai MCP server
Connects to your AI.
Any MCP-compatible agent gets the same governed connection, right where your teams already work.
Some of our integrations
Workflow tools
Jira, ServiceNow
Model providers
Anthropic, OpenAI
Agent tools
Copilot Studio, Claude Enterprise
Development environments
Azure AI Foundry, Amazon Bedrock, Databricks

Glossary

Testing & Validation

Testing & Validation

Testing & Validation

The systematic process of evaluating AI models against benchmarks, edge cases, and stress conditions to ensure they meet performance, safety, and compliance criteria.

The systematic process of evaluating AI models against benchmarks, edge cases, and stress conditions to ensure they meet performance, safety, and compliance criteria.

Updated

Encompasses unit tests for individual components, integration tests for data pipelines, regression tests against historical data, edge-case scenarios (adversarial, rare events), and stress tests on scalability and security. Validation includes statistical performance metrics, fairness audits, and compliance checks. Governance enforces that no model reaches production without passing a comprehensive test-and-validation checklist approved by independent reviewers.

Example

A credit-risk model’s testing suite includes: hold-out validation on recent loan data; stress tests with simulated economic downturn scenarios; bias tests across income and demographic groups; and API load tests. Only after passing all stages does the model receive final sign-off for deployment.

Govern the AI you actually use.

Govern the AI you actually use.

Govern the AI you actually use.

Start with what’s in use.

Start with what’s in use.

Start with what’s in use.

Book a demo and we’ll show you how Enzai finds the AI in use, gives each system an owner and keeps the record.

Book a demo and we’ll show you how Enzai finds the AI in use, gives each system an owner and keeps the record.