bitREC develops recommender systems for both off and online retailers enabling them to provide personalized content to their customers. Recommender systems allow retailers to increase sales volume (amazon generates ~35% of revenue via recommendation engine), minimize discount costs, provide relevant content to the customers. bitREC does not believe in general recommenders, which currently dominate the market. Different retail areas have different business goals, moreover data properties in different retail sectors vary tremendously and this requires special algorithms to be deployed in order to achieve optimal results. Currently bitREC is working in three retail sectors: Groceries, DIY, HORECA. Our scientists team was the first in the world to develop scalable algorithms that generated recommendations based not only on historical customer behavior data but also evaluated additional dynamic variables called contexts which dramatically influence customer behavior (e.g. weather, time of the day, day of the week). Netflix increased its recommendation precision by 200% after deploying context aware recommendation algorithms. We believe that similar results might be achieved in various businesses by using bitREC’s recommender system.