Driving better health outcomes.

A Machine Learning Pipeline

Genetica Serves as a Machine Learning Pipeline or AutoML to automate the process of applying machine learning to real-world decisions.

Former hockey great Wayne Gretzky was once asked what made him so good because he wasn't the fastest player and did not have the hardest shot? His answer: "I skate to where the puck is going to be, not where it has been." Data driven, predictive AI enables healthcare organizations to make decisions based on what the future holds. In today's rapidly changing world, the past is not necessarily the best predictor of the future. Gone are they days when healthcare organizations could assure success by relying on what was successful in the past when planning for the future.

The GeneticaAI Cortex Cognitive Engine© is a fully integrated end-to-end cloud-based AI application development and lifecycle management platform.

Cortex is a platform for generating domain-specific enterprise-class scalable mobile and web applications with built-in services for AI/machine learning, IoT, data lakes, GIS, and data mining.

Cortex supports domain experts to design, generate, deploy, and manage microservices and mobile apps that can communicate with each other and form complex applications utilizing language-agnostic APIs.

All external computing components of our platform are best-of-breed and open-sourced, ensuring the platform’s future proofing. It operates as a self-organizing computing cluster with a high degree of linear scalability. Its middle-tier application logic layer runs under a Node runtime environment. It can be configured to run on as many computing hosts as needed while maintaining and managing its “cluster state” via Kafka streaming engine. Data-tier runs on the back of MongoDB scalable sharding and replication architecture.

Instead of monoliths, Genetica’s applications are decomposed into smaller, decentralized services. These services communicate through APIs or by using asynchronous messaging (MQTT and Kafka). Applications scale horizontally, adding new instances as demand requires.

The platform’s runtime environment is built for data-intensive, real-time applications, meaning it is single-threaded, event-driven, and designed for elastic scale and automated self-management.

The application state is distributed in a self-organizing cluster of nodes. Operations are done in parallel and asynchronously. The system as a whole is resilient when failures occur. Deployments are automated and predictable. Monitoring and telemetry are built-in for gaining insight into the system.