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

AI Explainability

AI Explainability

AI Explainability

The extent to which the internal mechanics of an AI system can be understood and interpreted by humans.

The extent to which the internal mechanics of an AI system can be understood and interpreted by humans.

Updated

The suite of methods (surrogate models, feature-attribution, counterfactuals) and processes (documentation, user-friendly dashboards) that make AI decisions transparent, so stakeholders can understand, contest, and trust outcomes.

Example

A credit-scoring model uses SHAP to highlight which financial factors (e.g., “low credit history”) influenced a denial. Loan officers review these explanations alongside the AI’s recommendation, allowing applicants to correct inaccuracies and request human review.

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.