Karos develops and commercializes AI-based sustainable mobility systems for local authorities and corporations in order to fix commuting, which is most usually inefficient, lengthy and polluting in suburban areas. Karos leverages mobile and machine learning technologies to adapt carpooling to the specific constraints of short-distance trips and (ii) optimizes door-to-door commuting routes, offering the best options to reach destination mixing carpooling lines and mass transit lines. Therefore, Karos offers a new, efficient public transportation system, which translates into better public service for citizens, better use of resources, less impact on the environment.
The objective of Karos – Integration of a dynamic and predictive short distance carpooling offer into route planner services (Grant Agreement number: 744972), was to prove technical and commercial viability, conducting an analytical exercise to position the product. As such, its dimension was twofold: (i) test the economic viability of the platform among a larger pool of potential clients (user-acceptance), and (ii) to assess if the chosen commercialization approach will be conducive of the projected growth.