decisionOS allows you to go from raw data to a production-ready decision making application, or dApp, seamlessly. decisionOS is powered by Causal AI to ensure that the apps and causal models you produce are both accurate and trusted.

With decisionOS you get:

A suite of Python packages allow you to perform both identification of causal relationships, as well as estimation of causal effects.
decisionOS provides state-of-the-art techniques for performing both causal graph discovery and causal model discovery, with a particular focus on how to best embed human domain expertise throughout using human guided causal discovery.

decisionOS then provides tools to maximize the value derived from your causal graph and model. These include Causal Decision Intelligence Engines, which empower you to model different business critical scenarios, such as minimizing bias or determining the best course of action.

decisionOS also allows you to determine the causal effect of your actions, determine the root cause of outcomes, and assess, quantify, and fix the fairness of your data and models.

decisionOS exposes powerful application framework building tools, allowing you to transform pure Python into production-ready web applications.

These decisionApps can be delivered to business stakeholders, or domain experts allowing them to self-service actionable recommendations related to their business challenges.

decisionApps can be composed quickly using pre-built high level components, extensions and plugins. Additionally, decisionApps are highly interactive and have been designed to handle data driven tasks.

decisionOS is a cloud based platform for building an end to end decision making workflow. This starts from loading, preparing and exploring your data through the building of causal models to building a decision application. This workflow allows you to empower your partners in your business to make better decisions using the models that you build within decisionOS.

decisionOS takes care of all the infrastructure and environment management so you don't have to. This allows you to get started building causal models on a distributed Kubernetes cluster within minutes rather than weeks.