US$60B+ has been spent on the safety of Machine Learning (ML) assisted and fully autonomous driving systems to 2021. The industry knows pre-deployment testing is the last step to scale… but they have no way to quantify the safety of black box machine learning software against the unknowns. Instead automotives are stuck in endless years of unaffordable test mileage leaving behind the 1.35M global road users who are still dying in traditional accidents every year.

Introducing Rydesafely.

We’re the only platform to empower engineers to hyper-target tests to the quantifiable gaps in their machine learning systems. Our patented tech and cross-industry data structure has the unique ability to quantify system safety by mapping all the faults that can occur for a specified use case. Once they know the gaps, engineers can then auto-generate scenario tests targeting the hardest and unique vulnerabilities to their specific system.

In short, our method can deliver 10x faster AV deployment potentially bringing the industry forward decades and cutting billions of dollars in wasted test mileage. We’re fundraising to build out this data structure further and expand our current high powered team and advisors from Uber ATG, Rolls Royce, Jaguar Land Rover, Samsung and Huawei and beyond.