AI-powered patient engagement platform for hospitals and health systems
Get Well operates a digital patient engagement platform serving over 1,000 hospital sites and 10 million patients annually. The tech stack reveals a data-intensive, AI-forward organization: Kafka + Spark + Databricks + Snowflake for longitudinal analytics, Docker + Kubernetes for containerized ML infrastructure, and Epic + Cerner integrations for EHR workflows. Active hiring is engineering-heavy (13 of 23 open roles) with senior and staff-level concentration, and projects span smart-room AI, CI/CD automation for ML models, and observability—indicating a shift from legacy patient engagement toward real-time, AI-driven care navigation.
Get Well is a digital patient engagement platform owned by SAIGroup that helps hospitals and health systems activate patients across inpatient and ambulatory settings. The company delivers personalized care coordination and patient education through a combination of AI-driven navigation and clinical workflows, with longitudinal data analytics to track outcomes and cost reduction. Deployment spans over 1,000 hospital and clinical partner sites in the United States, with headquarters in Bethesda, Maryland. The platform integrates deeply with major EHR systems (Epic, Cerner) and operates at scale across cloud infrastructure (AWS, GCP, Azure, OCI).
Get Well uses Kafka, Spark, Databricks, and Snowflake for data pipelines; Docker and Kubernetes for containerized deployments; Epic and Cerner for EHR integration; and Python, JavaScript, and TypeScript for application development.
Get Well serves more than 10 million patients annually across over 1,000 hospitals and clinical partner sites in the United States.
Get Well's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.