Why Monitaur?
We know the way.

Monitaur leads the way in end-to-end AI and Machine Learning assurance. Our goal is to enable independently verifiable proof that your AI/ML systems are functioning responsibly and as expected.

Our cloud-based applications are SOC2 Type II certified to deliver comprehensive, user-friendly processes for your teams and entire AI/ML system lifecycles.

ML is unique

ML systems present a new paradigm of technology and business decisioning.

Individuals and organizations responsible for managing risk and compliance – including regulators – have not previously contended with such dynamic and variable models and systems.

ML is unique

Complex systems need complete solutions.

Because machine learning is making key decisions that affect people's lives, livelihoods, and opportunities, trust and confidence in AI/ML systems is paramount. There should be a reasonable ability to evaluate and verify their safety, fairness, and compliance.

Companies, regulators, and consumers all benefit from an objective method of assuring ML systems.

For Practitioners

ML models can behave unexpectedly, creating enormous liabilities and risk for unforeseen damages and reputational harm.

For Regulators

ML systems are opaque to non-technical professionals, requiring more ongoing attention from objective parties to ensure safety and compliance.

For Society

Unassured ML applications can lead to broad misconceptions and bias, undermining the long-term promise of the technology.

Pillars of ML

Three core pillars support an effective assurance and responsible use of AI/ML.


MLA requires clear understanding and documentation of the considerations, goals, and risks evaluated during the lifecycle of an ML application.


MLA requires every business and technical decision has the ability to be verified and interrogated.


MLA requires any ML application can be reasonably evaluated and understood by an objective individual or party not involved in the model development.

Machine Learning Assurance Framework

Creating assurance ML systems requires a continuous, coherent approach throughout the lifecycle of projects, as well as across your enterprise operations. Careful coordination of people, processes, and systems can create the clarity, confidence, and accountability that practitioners need.

Organizations can utilize the established, effective CRISP-DM steps and deploy detective controls vital for machine learning systems to drive a powerful framework for assurance and robust risk/control matrices.

Graphic with ML Assurance Steps and Controls

Steps Explained

Business Understanding
Data Understanding
Data Preparation
Graphic of Business Understanding Step and Controls
Graphic of Data Understanding Step and Controls
Graphic of Data Preparation Step and Controls
Graphic of Modeling Step and Controls
Graphic of Evaluation Step and Controls
Graphic of Deployment Step and Controls

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