Healthcare capacity, scheduling and billing software for health systems
Petal builds software that helps hospitals and health authorities match care supply to patient demand across scheduling, patient flow and billing. Its stack is unusually AI-forward for health IT: Azure AI Foundry, Azure AI Search and LangGraph sit alongside Cursor. Job posts describe an AI agent orchestration and evaluation buildout plus a semantic layer for AI-ready data, and current hiring leans toward support and customer success.
Petal is a privately held health technology company founded in 2010 and headquartered in Québec, Canada, with 201–500 employees. It sells to health authorities, hospitals, clinics and clinicians, covering patient flow, workforce planning, scheduling, care navigation and medical billing. The company reports more than 100,000 users across 3,100 hospitals and clinics. Its platform also redirects patients away from emergency departments toward more appropriate care settings. The product runs on TypeScript, Angular, NestJS and PostgreSQL across Azure and AWS.
Petal uses Python, Node.js, TypeScript, Angular, NestJS and PostgreSQL on Azure and AWS. It deploys with Kubernetes, Terraform, GitLab CI/CD and Argo CD, and builds AI with Azure AI Foundry and LangGraph.
Yes. Hiring is accelerating, with Canada-based openings concentrated in customer support and engineering. There are also roles in data, finance, product, sales and healthcare.
Petal is building an AI agent orchestration platform, along with lifecycle management and an evaluation framework. It is also implementing a semantic layer for AI-ready data products and designing customer health and segmentation models.
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Petal'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.