CARPL.ai has raised $10 million, about Rs 96 crore, in a Series A funding round led by the International Finance Corporation, the World Bank Group’s private-sector investment arm. Existing investor Stellaris Venture Partners and other undisclosed investors also participated. The company will use the capital for product development, strengthening its technology stack and expanding global sales and delivery teams. The raise comes as healthcare providers increase adoption of AI-enabled medical imaging tools and need platforms that can simplify deployment, management and monitoring across multiple AI vendors.
Founded in 2018 by Vidur Mahajan and officially launched in 2021, CARPL.ai provides a healthtech SaaS platform that allows hospitals and radiologists to deploy, manage and monitor multiple medical imaging AI applications through a single software interface. The company’s marketplace allows healthcare providers to browse, test and deploy commercial radiology AI models from different vendors. It says the platform hosts more than 300 AI applications from over 100 vendors.
The company is addressing a practical barrier in medical AI adoption. Hospitals and diagnostic networks may want to use multiple AI tools for different imaging needs, but each vendor can bring separate software, APIs, integrations, workflows and operational requirements. That fragmentation raises deployment costs and can slow adoption by radiology teams that need consistent workflows, predictable model monitoring and technology that fits into existing hospital systems. CARPL.ai’s marketplace model is designed to sit between the healthcare institution and the AI vendor ecosystem, creating a layer for testing, procurement, integration and ongoing oversight.
The funding is also a signal for Indian healthtech companies building infrastructure rather than single-point diagnostic tools. Medical imaging AI has advanced rapidly, but the market is crowded with specialised vendors across use cases such as radiology triage, chest imaging, neuroimaging, oncology, workflow prioritisation and quality checks. A platform that can aggregate those applications and help hospitals manage them may benefit from the broader shift toward AI procurement discipline in healthcare. For hospital CIOs and radiology administrators, the immediate question is often not whether an AI model performs well in isolation, but whether it can be integrated, monitored, audited and scaled safely across clinical workflows.
The participation of a development-finance institution gives the round an additional healthcare-access dimension, although the company’s business remains a commercial SaaS model. CARPL.ai’s global sales and delivery expansion plans indicate that the company is seeking to compete beyond the domestic healthcare market. If the marketplace can maintain vendor breadth while satisfying hospital requirements around clinical validation, interoperability, data handling and support, it could become part of the operating layer through which medical imaging AI moves from pilots into routine deployment.
