Closedloop is a data science platform built specifically for healthcare organizations. There are two core pillars to our technology. The first is an automated machine learning engine, which automates workflow and best practices associated with building a machine learning model while at the same time, providing transparency and control that the more advanced technical users are looking for. The second core pillar is a deep healthcare content library. This includes a catalog of model templates for common healthcare use cases. It includes thousands of clinically relevant attributes, which can be automatically derived from your raw data and used in analysis or in modeling. These are things like the frailty index, delirium index, medication adherence, comorbidities, etc. We also support automatic cleanup of medical codes support for various medical ontologies and of course support for all the common healthcare data types: claims, EHR, lab data, prescription data, and more taken together. These two pillars allow Closedloop customers to rapidly build highly accurate and explainable custom models for the use cases that are most important to them.