Insurance purchasing and management platform for residential and commercial properties
Get Covered runs a full-stack insurance tech application built on Angular, Rails, Python, and AWS, now integrating OpenAI and Anthropic LLMs into product workflows. The engineering-heavy hiring mix—10 engineers across mid and senior levels—reflects active work on infrastructure (observability, blue/green deployments, CI/CD pipelines) and AI integration, suggesting a shift from pure automation toward intelligent workflows to handle the manual labor inherent in insurance administration.
Get Covered operates an insurance technology platform that automates property coverage purchasing and policy management for tenants, property managers, and insurance agencies. Founded in 2017 and based in New York, the company serves residential and commercial clients who need simplified, rapid policy issuance. The product handles enrollment, underwriting, and ongoing policy maintenance through a web interface. The stack spans multiple backend languages (Rails, Python, Django, Node.js) and uses PostgreSQL and Redis for state, deployed on AWS with container orchestration via ECS.
Frontend: Angular, TypeScript, Kendo UI, HTML5. Backend: Ruby on Rails, Python (Flask, Django), Node.js (Express). Data: PostgreSQL, Redis. Infrastructure: AWS (ECS, RDS, ElastiCache), Docker, Nginx. Testing: RSpec, Cypress, Jasmine, Selenium, Playwright. CI/CD: CircleCI, Jenkins.
Core projects include AI/LLM integration for product workflows, automated test framework development, CI/CD pipeline improvement, observability stack implementation, blue/green deployment rollout, and insurance workflows microservices architecture.
Get Covered'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.