ModelOp

Founded 2018

Manage, govern, and track machine learning models and generative systems across enterprises

Pricing model

Demo

You can use ModelOp to track, govern, and manage enterprise artificial intelligence assets across their entire lifecycle. It provides a central inventory for traditional machine learning models, generative applications, and autonomous agents across cloud or on-premise environments. The system automates compliance checks, aligns engineering workflows with corporate risk policies, and monitors token costs, operational risk, and business return on investment. Teams can audit models against industry regulations, document evidence automatically, and coordinate deployments between data scientists, risk teams, and engineering units without bottlenecks.

What ModelOp does

Register new machine learning models in the central enterprise catalogEvaluate model compliance against internal and regulatory safety standardsMonitor token usage and computing costs across generative AI applicationsExport audit logs and compliance evidence for legal reviewsAssign risk ratings to internal and third-party vendor algorithmsAutomate handoffs between data scientists, risk officers, and IT teamsCentralized system of record for all enterprise machine learning models and generative agentsAutomated governance workflows with policy verification before production releaseReal-time tracking of token usage, operational costs, and business ROIAutomated generation of audit-ready compliance documentationRisk rating automation to evaluate model exposure and vulnerabilityInteroperability framework connecting development environments to governance stacksLifecycle management for traditional, generative, and third-party vendor AI