ML Integrity, Delivered

Machine learning models fail.
Prevent bad outcomes with the only end-to-end solution.

Get Started

Introducing the ML Model Attribution Challenge

Read more

Trusted by data science teams at

The Problem

Models fail in the real world

AI is eating the world, but the ML models that power intelligent applications are prone to failure. Data science teams are always on alert, reacting to changes observed in dashboards and writing ad hoc tests. The need to constantly firefight model errors introduces risk and makes it difficult to apply ML at scale.
Example of some errors that models may presentCornercase inputs model errorBroken data pipilines model errorBiased predictions model errorAdversarial inputs model errorUnexpected model behavior errorDistributional shift model error
Unexpected model behavior errorAdversarial inputs model errorCornercase inputs model errorBroken data pipelines model errorDistributional shift model errorBiased predictions model error
The Solution

E2E Platform for ML Integrity

ML Integrity means that your models work as intended. It requires a rigorous approach to testing, monitoring, and improving your models. Robust Intelligence provides a platform that ensures ML Integrity throughout the model lifecycle
Key Components

The Robust Intelligence Platform

AI Stress Testing

Before models are put into the wild, we proactively run hundreds of tests to automatically identify potential issues and suggest improvements for model production readiness.
Read More

AI Firewall

Once models are ready for deployment, we automatically configure and deploy a wrapper around your model to prevent bad data from entering your system in real time.
Read More

AI Continuous Testing

Monitor the behavior of models in production to identify issues, understand when it’s time to retrain a model, and automate root cause analysis of model failure.
Read More
Proof Points
increase in data science team productivity through automated testing and monitoring of ML models
more models deployed into production per year
decrease in the mean time to resolution of production machine learning issues
reduction in cost and time for model compliance verification