Our mission is to build Structural Causal Models Inference Machine platform capable to answer “What if?” kind of interventional or counterfactual questions. Our solutions and approach are already capable address the issue of trust in current AI technology. The issue of trust in AI is on top of mind for many technology developers and providers. AI-powered systems hold enormous potential to transform the way we live and work but also exhibit some vulnerabilities, such as exposure to bias, lack of explainability, and susceptibility to adversarial attacks. These issues must be addressed for AI services to be trusted. Several core pillars form the basis for trust in AI systems: fairness, robustness, and explainability. Impartial AI systems can be credibly believed not to contain biased algorithms or datasets, or to contribute to the unfair treatment of certain groups. Robust AI systems are presumed safe from adversarial attacks and manipulation. And explainable AI systems aren’t a “black box” — their decisions are understandable by both researchers and developers.