AI systems are inherently unpredictable and unreliable compared to traditional software, creating risks for enterprises deploying AI applications. Traditional software testing methods are inadequate for AI, as they assume a predictable system with well-defined inputs and outputs. This unpredictability makes it difficult for AI teams to ensure the safety, reliability, and security of their AI applications throughout the AI software lifecycle.

Distributional provides an enterprise AI testing platform that enables AI teams to collect, test, and analyze data throughout the AI software lifecycle, ensuring compliance, governance, and improved system performance. The platform uses adaptive preference learning to automate data augmentation, test selection, and test calibration, allowing teams to define desired behavior, detect behavior shifts, and gain clear insights into what's driving changes. Distributional's framework enables AI application teams to collect and augment data, test on this data, alert on test results, triage these results, and resolve issues. The platform integrates with existing AI infrastructure and can be deployed in a virtual private cloud (VPC).

Automated data augmentation to expand test coverage

Intelligent test selection to focus on the most critical areas

Adaptive test calibration to minimize false positives and negatives

Behavioral fingerprinting from runtime logs and existing metrics to track behavior changes over time

Comprehensive testing that covers every attribute of every part of the AI application

Clear insights into what's driving changes, down to specific result sets

Automated alerts on behavior changes

Dashboards to analyze results, triage failures, capture an audit trail, and report outcomes for governance

Integration with existing AI infrastructure and deployment in a virtual private cloud (VPC)

Distributional is designed for enterprise AI application teams that need to ensure the safety, reliability, and security of their AI applications.