AI clinical documentation that suggests diagnoses from EHR data
Regard builds AI that reads hospital EHR data from Epic and Cerner, suggests diagnoses and drafts clinical notes, built on Python, Go and FHIR. Its data layer (Spark on EMR, Iceberg, Dagster, ClickHouse) is a lakehouse-style stack, and recurring pains around Spark and pipeline costs show cost control is now a priority. Hiring is accelerating and weighted toward support and sales leadership, which points to scaling hospital deployments more than core R&D.
Notable leadership hires: Director
Regard is a New York-based healthcare technology company founded in 2017, with 51–200 employees. Its product integrates with hospital electronic health records to surface clinical context, recommend diagnoses and draft documentation for physicians. The company targets hospitals seeking to reduce physician documentation burden and improve financial performance. Engineering runs on AWS with Kubernetes, Python, TypeScript and Go, and uses large language models from OpenAI and Anthropic. Hiring is concentrated in the United States.
Regard runs on AWS with Python, Go, TypeScript, React, FastAPI, PostgreSQL and Kubernetes. Its data stack includes Apache Spark on EMR, Iceberg, Dagster and ClickHouse. It integrates with Epic and Cerner via FHIR.
Yes. Regard's stack includes OpenAI and Anthropic, and job posts reference integrating generative AI technologies into its diagnosis and documentation products.
Yes, and hiring is accelerating, all in the United States. Openings lean toward support and sales, with additional data and engineering roles and many manager- and director-level positions.
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Regard'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.