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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

Explainability Techniques

Explainability Techniques

Explainability Techniques

Methods used to interpret and understand the decisions made by AI models, such as LIME, SHAP, and saliency maps.

Methods used to interpret and understand the decisions made by AI models, such as LIME, SHAP, and saliency maps.

Updated

A toolbox of model-agnostic (LIME, SHAP) and model-specific (saliency for CNNs, attention visualization) approaches that generate feature attributions, visualize decision pathways, and produce counterfactual explanations. Governance best practices include selecting techniques appropriate to the model type, validating explanation accuracy, and integrating explanations into end-user workflows.

Example

A retail chain uses SHAP to explain its customer-churn predictions: each record shows “high monthly spend” and “recent service complaints” as top contributors. Customer-success managers use these explanations to tailor retention offers, improving customer satisfaction.

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.