Atomwise vs Niramai

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Atomwise employs 75 people, while Niramai has 50-200 employees.

Atomwise has raised more in total funding at $219M, compared to Niramai's $17M — a gap of $202M. Combined, the two companies have completed 5 known funding rounds.

Niramai holds the higher Awaira Score at 60/100, compared to Atomwise's 53/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 7-point gap that reflects meaningful differences in scale or traction.

Atomwise was founded by Abraham Heifets in 2012. Niramai was founded by Geetha Manjunath in 2016. Visit each company's profile on Awaira for a full founder biography.

Atomwise: Atomwise is an artificial intelligence company focused on drug discovery and development, founded in 2012 and headquartered in the United States. The company uses AI and computational chemistry to identify and optimize potential drug candidates, reducing the time and cost associated with traditional pharmaceutical research methods. Atomwise's platform leverages machine learning algorithms to screen vast chemical libraries and predict molecular interactions, enabling faster identification of promising compounds for various therapeutic areas including oncology, infectious diseases, and genetic disorders. The company has secured $219 million in total funding and operates at the Series B stage, though its current valuation remains undisclosed. Atomwise's approach combines physics-based molecular modeling with machine learning to simulate how potential drugs interact with disease targets. The platform has been applied to multiple drug discovery projects across both internal programs and partnerships with pharmaceutical companies and research institutions. Atomwise competes in the growing computational drug discovery sector alongside companies utilizing AI for pharmaceutical development. The company's technology addresses significant pain points in drug development, including extended timelines and high failure rates. Its trajectory reflects the increasing adoption of AI-driven approaches in healthcare and pharmaceutical industries, positioning it within a broader trend toward computational acceleration of biomedical research and development processes. Atomwise applies physics-informed machine learning to molecular simulation for drug discovery, combining computational chemistry with AI to accelerate preclinical pharmaceutical development. Niramai: Niramai has developed a radiation-free, non-contact breast cancer screening solution that uses thermal imaging combined with AI-powered image analysis to detect early-stage breast cancer, addressing the significant access and cost barriers that prevent widespread mammography screening in low- and middle-income countries. The technology works without X-ray radiation, requires no radiologist for initial interpretation, and can be deployed in mobile screening units.\n\nThe company raised approximately $17M in Series B funding and has conducted clinical validation studies demonstrating sensitivity and specificity metrics competitive with mammography for early detection. Niramai has deployed its screening solution at hospitals, corporate wellness programs, and rural health camps across India.\n\nBreast cancer is the most common cancer among Indian women, yet screening rates remain critically low due to equipment costs, specialist shortages, and cultural barriers to mammography. Niramai's AI-powered thermal screening addresses each of these barriers simultaneously, positioning the company at the intersection of AI healthcare innovation and a major Indian public health challenge.

Atomwise was founded first in 2012, giving it 4 years of additional market experience. Niramai was founded later in 2016. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Atomwise has approximately 75 employees, while Niramai has approximately 50-200. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Atomwise and Niramai are direct competitors — both operate in the AI Healthcare space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.