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 Retraining

Model Retraining

Model Retraining

The process of updating an AI model with new or refreshed data to maintain performance and compliance as data distributions evolve.

The process of updating an AI model with new or refreshed data to maintain performance and compliance as data distributions evolve.

Updated

A scheduled or trigger-based pipeline that ingests new labeled data (e.g., recent transactions), retrains the model with updated parameters, validates against current benchmarks, and deploys the new version. Governance defines retraining frequency, approval gates (automated tests, validation reviews), rollback protocols, and documentation requirements to ensure controlled updates.

Example

A logistics company’s demand-forecast model retrains monthly on the past 90 days of shipment data, runs automated validations (accuracy, drift, fairness), and deploys the updated model during off-peak hours. If post-deployment metrics drop, the system rolls back to the previous version.

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.