Medra

Medra is a San Francisco-based company developing a platform to accelerate scientific research by automating laboratory tasks through artificial intelligence and robotics. The company was founded by Michelle Lee, who serves as CEO. Lee holds a PhD from the Stanford AI Lab and was slated to become an assistant professor at New York University before leaving academia to establish Medra. Her background includes work at NVIDIA, SpaceX, and McKinsey. The founding team includes engineers with experience from companies like Tesla, SpaceX, and Neuralink.

Medra's core offering is its Continuous Science Platform, a system that integrates robotics and AI to manage and execute laboratory experiments. The platform consists of two main components: "Physical AI" and "Scientific AI". The Physical AI component uses general-purpose robotic systems to physically perform tasks, such as pipetting and handling biological samples, interfacing with standard lab instruments. This system is designed for flexibility, allowing scientists to adapt workflows using natural language instructions. The Scientific AI component then interprets the data and results from these experiments, identifies patterns, and suggests improvements or new experimental paths, creating a continuous, closed-loop learning system. This integration aims to solve the bottleneck of data scarcity in scientific research by enabling experiments to be run at a much larger scale and faster pace than is possible manually.

The company's business model involves deploying its systems to clients and is also planning to open its own fully automated lab. Medra serves biopharmaceutical companies and research organizations, helping them design new antibodies, develop gene therapies, and run cell-based assays. The company has established collaborations with prominent partners, including Genentech, Addition Therapeutics, and Cultivarium. In December 2025, Medra announced it had raised $52 million in a Series A funding round led by Human Capital, with participation from investors such as Lux Capital, Neo, and Menlo Ventures.

Keywords: physical AI, lab automation, scientific AI, robotics, drug discovery, life sciences, continuous experimentation, automated laboratories, scientific data generation, robotic workcell, biotech automation, AI-driven research, experiment optimization, gene therapies, antibody design, high-throughput screening, cell-based assays, laboratory instruments, data scarcity, natural language protocols