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

Model Validation

Model Validation

Model Validation

The evaluation activities (e.g., testing against hold-out data, stress scenarios) that confirm an AI model meets its intended purpose and performance criteria.

The evaluation activities (e.g., testing against hold-out data, stress scenarios) that confirm an AI model meets its intended purpose and performance criteria.

Updated

A set of pre-deployment checks including: back-testing on unseen data, stress-testing under extreme or adversarial conditions, fairness and calibration assessments, and sensitivity analyzes. Validation reports document methodologies, results, and any limitations. Governance requires independent validators, clear validation criteria, and formal sign-off before production release.

Example

A credit-scoring model undergoes validation by an independent team: they test it on a two-month hold-out set, simulate economic downturn scenarios, evaluate fairness across income brackets, and certify that performance and fairness metrics meet the bank’s policy thresholds before approval for live use.

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.