[Ajs1]

Hi, one of the founders here: The service is designed to not require specific data models, because that does not scale, nor does it keep up with changes in application behavior. Instead, the ML engine learns data structures, normal behavior of logs and metrics, and normal correlations between them for each app deployment on the fly. Then when things break it does a very good job of generating incidents. We make user feedback easy, so if we are "over-eager" in detecting a certain kind of incident, your response trains the ML quickly. We do improve the ML engine with experience of course (and have added some user controls), but now have dozens of applications using us, and cumulatively have over a thousand successfully detected incidents under our belts.