Cardiac remote monitoring platform for implantable device data
Implicity runs a cardiac remote monitoring and research platform that normalizes data from implantable cardiac devices across manufacturers for hospitals and clinics. Its data stack (AWS Glue, dbt, Apache Iceberg, Athena, BigQuery) points to a lakehouse-style analytics layer. Adopting n8n and MCP while reworking its Temporal setup signals heavier investment in workflow orchestration, matching pain points around ingestion modernization and orchestration reliability.
Implicity is a digital MedTech company founded in 2016 by a cardiac electrophysiologist and headquartered in Cambridge, Massachusetts. It provides hospitals and clinics with a software platform for remotely monitoring cardiac patients, aggregating and standardizing data from implantable cardiac electronic devices made by major manufacturers. The company develops AI and knowledge-based algorithms aimed at anticipating patient health status and easing clinician workload, with a focus on chronic heart failure care. Implicity employs 51–200 people, serves facilities in the United States and Europe, and hires in the United States and France.
Implicity builds on Python, TypeScript, Node.js, NestJS and Angular on AWS, with Docker, Kubernetes and GitLab CI/CD, plus PostgreSQL, MongoDB, dbt, Apache Iceberg, Athena and BigQuery for data.
Yes. Hiring is accelerating and engineering is the largest department in its open roles, followed by product, with positions in the United States and France.
Current initiatives include go-to-market launches, self-supervised learning pipelines, SNDS data exploration via France's Health Data Hub, and maturing its Figma-based design system.
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Implicity'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 →
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