Prevention is a key instrument in fighting cybercrime. Technological solutions that actively inform, enlighten and protect consumers and their representative bodies are an important addition to preventive measures both in terms of capacity and speed in fighting illegitimate subscription services, counterfeit goods and fraudulent e-commerce offerings. AIT provides an integrated pipeline for stakeholders reaching from data acquisition to Machine Learning (ML) based methods and Open Source tools and services that are applied at scale such as the fake-shop detector that are both, closely integrated in the day to day use cases of stakeholders (e.g. Watchlist Internet, federal ministries, chamber of labor) as well as directly delivered through user facing applications to the public. Hereby assessing consumer needs with purposeful methods such as gamification, creating deriving and extracting potential features from heterogeneous data sources at scale as well as through community driven knowledge curation processes, a deep understanding of the application domain itself and collaborations with key industry and KMU partners together with existing and tailormade tools for applied and explainable Machine Learning allow us to provide meaningful models, data-based evidence, factual insight, publications and open source solutions in a variety of challenging and consumer protection related topics reaching from gender specific inequalities in the field of personal and dynamic pricing to the automated flagging of fraudulent eCommerce in the DACH region.