Rejecting the highly-prevalent black box approach, we employ our proprietary “Explainable AI” or "XAI". While popular, black box methods have distinct limitations. In order to verify black box recommendations, extensive testing and strategy are required to ensure that models can work consistently, accurately, and can generalize to new data. Many of today’s machine learning algorithms fail to achieve goals because of the black box issue. Our explainable AI outputs are delivered with an easy to understand explanation as to why the algorithm came to the conclusions that it did, allowing our ventures to leapfrog competitors and make better business decisions.

One of the biggest problems holding back the further proliferation of AI in many sectors is the issue of scalability. Through the Internet of Things (IoT) organizations have access to increasing amounts of data, but often their traditional business intelligence systems can't take full advantage of that data. Our algorithms are scalable to very large input data sets, multiple users, variables, and more, allowing our start-ups to deliver breakthroughs in industries that have previously not been able to benefit from AI.