Niramai vs Qure.ai

Qure.ai has a disclosed valuation of $270M, while Niramai's valuation is not publicly available, making a direct size comparison difficult. Qure.ai employs 250 people.

Qure.ai has raised more in total funding at $157M, compared to Niramai's $17M — a gap of $140M. Combined, the two companies have completed 5 known funding rounds.

Qure.ai holds the higher Awaira Score at 70/100, compared to Niramai's 60/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 10-point gap that reflects meaningful differences in scale or traction.

Niramai was founded by Geetha Manjunath in 2016. Qure.ai was founded by Prashant Warier in 2016. Visit each company's profile on Awaira for a full founder biography.

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. Qure.ai: Qure.ai is an Indian artificial intelligence company founded in 2016 that develops machine learning solutions for diagnostic imaging in healthcare. The company specializes in computer-aided detection and diagnosis systems that analyze medical imaging data including chest X-rays, CT scans, and mammograms to identify abnormalities and assist radiologists in clinical decision-making. Its platform uses deep learning algorithms trained on large datasets to detect conditions such as tuberculosis, COVID-19, lung cancer, and other pathologies. Qure.ai's products are deployed across hospitals and diagnostic centers in India and internationally, serving both institutional healthcare providers and diagnostic chains. The company has secured $157 million in total funding and operates at Series D stage with a valuation of $300 million as of recent rounds. Its technology addresses the shortage of radiologists in developing markets while improving diagnostic accuracy and turnaround times. Qure.ai competes in the medical AI space alongside companies like IBM Watson Health, Zebra Medical Vision, and various regional diagnostic AI providers. The company has demonstrated strong traction in the Indian healthcare market and has expanded its reach to international markets. Its growth trajectory reflects increasing adoption of AI-assisted diagnostic solutions globally and the rising demand for scalable healthcare technologies in resource-constrained settings. Qure.ai focuses specifically on diagnostic imaging AI for markets with radiologist shortages, positioning it distinctly within underserved geographies.

Both Niramai and Qure.ai were founded in the same year — 2016. Despite sharing a founding year, they may have launched at different times within that year, which can matter in fast-moving AI markets.

Niramai has approximately 50-200 employees, while Qure.ai has approximately 250. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Niramai and Qure.ai 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.