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

XAI Metrics

XAI Metrics

XAI Metrics

Quantitative or qualitative measures (e.g., feature importance scores, explanation fidelity) used to assess the quality and reliability of AI explanations.

Quantitative or qualitative measures (e.g., feature importance scores, explanation fidelity) used to assess the quality and reliability of AI explanations.

Updated

Metrics that evaluate explanation properties such as fidelity (how well explanations match the true model behavior), stability (consistency of explanations under similar inputs), comprehensiveness (coverage of key features), and simplicity (conciseness for users). Governance uses these metrics to benchmark explanation methods, set acceptance thresholds, and track improvement over time.

Example

An e-commerce platform’s fraud-alert explanations are scored for fidelity by measuring the correlation between feature-attribution rankings and actual model sensitivity. Only explanation methods with fidelity > 0.85 are approved for end-user dashboards, ensuring reliable insights for investigators.

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.