Exclusive: Alterra Labs announces $12.5 million seed round to build training environments for medical AI
Alterra Labs, a New York startup building simulated medical environments to test and train AI models, is announcing a $12.5 million seed round led by Oak HC/FT, with participation from Flare Capital Partners and Company Ventures.
Key points
- Alterra turns clinicians’ assessments into scoring criteria and rewards used in reinforcement learning, which encourages models to repeat stronger decisions and avoid behaviors experts have flagged as unsafe.
- He studied machine learning before turning to medicine, pursued healthcare-focused machine learning research at the National Institutes of Health and completed three years at Columbia Medical School.
- The company evaluates models on medical tasks to identify where they fail, then turns those failures into training exercises.
“It lacks a lot of judgment.” Alterra says it works with several frontier labs, hospitals, medical societies and applied AI companies.
Frontier labs have yet to build deeply into those environments, he said.
Additional details
- Kapoor expects that work to require repeated testing and training as models gain capabilities and medical knowledge changes.
- From healthcare applications to model training Kapoor started the company in 2025 while attending Columbia Medical School.
- Vig Chandramouli, the Oak HC/FT partner who led the investment, said Oak saw a pattern across the healthcare AI companies it has backed—adapting models for medical work took more than data.
- The company also builds environments for tasks involving medical imaging, clinical trials and telehealth.
- Akash Kapoor, Alterra’s founder and CEO, told Fortune that today’s models can learn to operate the software while still struggling with the clinical judgment behind each action.
How this summary was made: extractive summary — built from the sources' own reporting. It is drawn only from the reporting of the sources listed below — read the originals for full detail. How we summarize →
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