Continuous Development: AI Firewall
Protect your model in real time
Protect production models against undesirable results by inspecting inputs and outputs in real time.


Real-time protection against anomalous data
Production models will inevitably receive “bad” data that they weren’t trained to support. Our AI Firewall can be configured to intelligently flag, block, or sanitize data points before they ever reach your model. This helps ensure that all models perform as intended.
Automate the validation of your data and model pipeline
Manual validation of every new dataset and model can be incomplete and take a painstaking amount of effort. Our AI Firewall automates the task of testing data quality. As each model is deployed, we auto-configure a custom set of requirements, ensuring trust and reducing time spent writing custom validation logic.


Flexible treatment of anomalous data
Robust Intelligence uses the test results from continuous validation during development to automatically configure your AI Firewall, but there is customizability in the treatment of aberrant data. Choose to change the default configuration to flag, block, or impute identified bad data points.
Reduce model downtime
All models eventually need to be retrained. The AI Firewall runs a comprehensive suite of validation logic on incoming production data in real time. It's able to surface key issues and alert on especially problematic data points so that the user is able to take proactive remediative actions. This end result is reduced model downtime.

Intelligent model vulnerability protection
Our AI Firewall is automatically configured from the weaknesses discovered during AI Stress Testing.
Blazing fast real-time processing
Protect your model against bad data points in near real-time, introducing minimal latency.
Simple to deploy
Deploy in a single line of code with our easy-to-use Python library interface.
Integrate seamlessly
Connect into your production AI framework, with hooks into model inference as well as downstream monitoring applications.