Autonomize AI, an Austin, Texas-based trusted artificial intelligence (AI) company for healthcare and life sciences, announced that it closed a $4 million seed round to support the development and scaling of the company’s AI platform.
Asset Management Ventures led the round, with participation from ATX Venture Partners, Loop Ventures, and The Next Practices Group.
This funding will be used to boost its R&D and commercialization efforts. Launched in 2022, Autonomize AI provides software products to lower costs and improve operational efficiencies for healthcare and life sciences organizations.
“Despite the abundance of data and the availability of cutting-edge technology like AI, healthcare is broken for the patient,” said Ganesh Padmanabhan, Autonomize AI founder, and CEO. “We are putting the patient back in the center of the healthcare ecosystem by empowering every knowledge worker with insights, content and context to do their best work and drive 100-times better patient outcomes. We are assembling some of the best scientists, researchers, and engineers to solve this critical problem.”
Company: Autonomize, Inc.
Round: Seed Round
Funding Month: May 2023
Lead Investors: Asset Management Ventures
Additional Investors: ATX Venture Partners, Loop Ventures, and The Next Practices Group
Company Website: https://autonomize.ai/
Software Category: AI-driven Solutions for Healthcare and Life-Sciences
About the Company: Founded in 2022, Autonomize AI specializes in AI solutions for healthcare and life-sciences organizations. The company boasts ready-to-deploy AI products designed with baked-in trust and security. Its platform uses large language models to analyze and interpret unstructured data, with evidence and explainability, and offers key generative AI capabilities to summarize and visualize information clearly and concisely. Autonomize AI’s products can be integrated into existing workflows through APIs. The company’s solutions are built on a secure cloud-based platform optimized for the healthcare industry that supports multi-modal data types and uses pre-trained medical large language models (LLMs).