Gravie operates a health insurance platform serving small and midsize employers with self-funded plan options. The tech stack reveals a hybrid architecture—Java/TypeScript services backed by Kafka, Flink, and Debezium for streaming data—alongside heavy reliance on enterprise workflow tools (Salesforce, Agiloft, Conga Composer, NetSuite). Active projects around regulatory change management, forecasting models, and a next-generation operational data platform suggest Gravie is building internal analytics infrastructure to handle compliance complexity and operational visibility, while pain points around moving from batch to real-time data processing indicate the platform is transitioning from manual/periodic reporting to continuous insights.
Notable leadership hires: Director Operations
Gravie is a health insurance provider founded in 2013, headquartered in Minneapolis, Minnesota, and serving employers with 201–500 employees across the company. The core product is self-funded health plans designed for small and midsize employers who traditionally lacked options for flexible, cost-transparent coverage. Gravie's primary go-to-market model involves direct outreach to employers and partnerships with benefits brokers. The organization is scaling aggressively, with 25 active roles (21 posted in the last 30 days) concentrated in sales, finance, and operations—signaling growth in customer acquisition and plan administration infrastructure.
Gravie's core platform is built on Java and TypeScript, with Kafka and Apache Flink for event streaming. Back-office systems include Salesforce, NetSuite, ADP, and Rippling for HR/payroll. Document generation and workflow automation run on Agiloft and Conga Composer.
Active projects include regulatory change management, forecasting models, a next-generation operational data platform, and unifying data across systems. Gravie is also developing new product features within its insurance plan portfolio and building broker engagement tools.
Gravie'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.