Car insurance is traditionally priced on the basis of self-reported information from the insured person, most importantly age, license age, postal code, engine power, use of the vehicle, and claims history. However, these observable risk factors are only proxy variables, not reflecting current driving habits and driving style. We are going to utilize the drivers' telematics data to compute a more competitive premium according to how far and how well they drive. We take it a step further to provide predictive analytics based on the users' personality, preferences, and behavior through ontology (behavioral-pattern) modeling using deep tech. To drive user growth, iLyF also provides contextual rewards and prescriptive content to earn cash rewards and discounts while staying healthy and safe.