We became fascinated by the idea of making Trusted AI available to Space 2.0, and we already knew that machine learning models are as good as the data used for building them. We have developed a Data-Centric AI framework that has changed the paradigm from searching for the best machine learning model to exploring the best data for building that. This includes building digital-twin systems to augment the data. We have become the pioneer in incorporating domain expertise and physics into the machine learning models for building Trusted AI. FuturifAI Data-Centric AI development platform has proven its capabilities in various global data science challenges, which require incorporating domain expertise into machine intelligence. To name recent a few, Collision Avoidance and Spot the GEO space objects challenges organised by European Space Agency and the Amazon-MIT Last-Mile challenges have brought international recognition to the FuturifAI Studio. We have worked with Equatorial Launch Australia, Stark Engineering, Neuraspace, Grodan, RMIT, University of Auckland, and UNSW. As FuturifAI, we are looking forward to partnering with our fellow Australian companies to bring trusted machine intelligence in use with confidence.