Building an AI solution, if the data is unlabeled and the labeling of the full data set is too expensive, is a more then complex task. In order to overcome this challenge, GfK uses a two-layer approach similar to active learning. In the first step we build a model to propose a relatively small subset of the data that should be annotated by the market experts that will work with the solution. Then, to further reduce the needed involvement we build a second model on the annotations to minimize their involvement for the future. The presentation will showcase how two-layer approach helped GfK to increase data quality while minimizing the needed human labelling effort. Furthermore, we will discuss the challenges and benefits of this approach. Finally, there will be a deep dive into the code, the architecture and continuous evolution pipeline for the model.