Distributional, a San Francisco, CA-based modern enterprise platform for AI testing, raised $19m in Series A funding.
The round was led by Two Sigma Ventures with participation from Andreessen Horowitz, Operator Collective, Oregon Venture Fund, Essence VC, Alumni Ventures and dozens of angel investors.
Founded in September 2023 by by Scott Clark, CEO and other experts with experience building, optimizing, and testing AI systems at Bloomberg, Google, Intel, Meta, SigOpt, Slack, Stripe, Uber and Yelp, Distributional is building a modern enterprise platform for AI testing. The platform, built to test the consistency of any AI/ML application, especially generative AI, which is particularly unreliable since it is prone to non-determinism, or varying outputs from a given input, helps automate AI testing with intelligent suggestions on augmenting application data, suggesting tests, and enabling a feedback loop that adaptively calibrates these tests for each AI application being tested.
Prominent features include:
- Extensible Test Framework: Distributional’s extensible test framework enables AI application teams to collect and augment data, test on this data, alert on test results, triage these results, and resolve these alerts through either adaptive calibration or analysis driven debugging. This framework can be deployed as a self-managed solution in a customer VPC and is fully integrated with existing datastores, workflow systems and alerting platforms.
- Configurable Test Dashboard: Teams use its configurable test dashboards to collaborate on test repositories, analyze test results, triage failed tests, calibrate tests, capture test session audit trails and report test outcomes for governance processes. This enables multiple teams to collaborate on an AI testing workflow throughout the lifecycle of the underlying application, and standardize it across AI platform, product, application and governance teams.
- Intelligent Test Automation: Distributional makes it easy for teams to get started and scale AI testing with automation of data augmentation, test selection and calibration of these steps in an adaptive preference learning process.
FinSMEs
08/10/2024