Electro-mechanical automation for histology and pathology labs
Clarapath builds SectionStar and TrimStar Pro—electro-mechanical systems that automate tissue-sample preparation and handling in pathology laboratories. The tech stack spans embedded systems (C/C++, FPGA, ROS, RTOS) paired with cloud infrastructure (AWS, Azure, GCP) and modern web frontends (React, Angular, Node.js), indicating a product that combines hardware control logic with networked diagnostic workflows. Active hiring across embedded engineering, logistics, and field deployment reflects scaling from product validation into operational delivery.
Clarapath is a medical robotics company headquartered in Hawthorne, New York, focused on automating laboratory workflows in histology and pathology. The core product, SectionStar, is an electro-mechanical system designed to prepare and organize tissue samples for pathologist review, with a companion device, TrimStar Pro, handling sample trimming. The company operates across hardware design (electrical and mechanical systems), embedded control software, cloud connectivity, and field deployment. Current priorities center on device reliability, continuous product refinement, and cost reduction for laboratory customers.
SectionStar, an electro-mechanical system that automates tissue-sample preparation in histology labs. TrimStar Pro handles sample trimming. Both aim to improve diagnostic accuracy and reduce laboratory costs.
Embedded systems (C/C++, FPGA, ROS, RTOS), cloud platforms (AWS, Azure, GCP), web frontends (React, Angular, Node.js), and backend services (Node.js, Ruby, Java, PostgreSQL, MongoDB). Recently adopting RTOS for real-time control.
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Clarapath'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.