Aidence vs Niramai

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Aidence employs 1-50 people, while Niramai has 50-200 employees.

Aidence has raised more in total funding at $20M, compared to Niramai's $17M — a gap of $3M.

Niramai holds the higher Awaira Score at 60/100, compared to Aidence's 45/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 15-point gap that reflects meaningful differences in scale or traction.

Aidence was founded by Jeroen Vendrig in 2016. Niramai was founded by Geetha Manjunath in 2016. Visit each company's profile on Awaira for a full founder biography.

Aidence: Aidence develops AI radiology software for lung cancer detection, building CE-marked and FDA-cleared deep learning algorithms that analyse CT scan images to identify and characterise pulmonary nodules requiring clinical follow-up. The Amsterdam company tools assist radiologists in national lung cancer screening programmes and in routine clinical CT reading, providing AI-generated nodule measurements, growth tracking, and malignancy risk scores that reduce reader variability and improve early detection rates.\n\nThe company raised approximately $20 million in venture funding and has deployed its Veye Chest algorithm across multiple European radiology networks and hospital systems participating in national lung cancer screening initiatives. Aidence received CE marking for its software as a medical device and has published clinical validation studies demonstrating performance that is non-inferior to specialist radiologist reads on lung nodule detection tasks.\n\nAidence competes in the AI radiology market alongside Annalise AI, Enlitic, Behold.ai, and Lunit, all of which target chest X-ray and CT reading assistance. The lung cancer screening market has expanded significantly as multiple European countries and the United States have implemented or planned national screening programmes for high-risk smokers, creating a growing workflow automation opportunity for AI lung nodule detection tools. The company was acquired by Coreline Soft, a South Korean medical AI company, as part of a broader consolidation in the AI radiology market. 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.

Both Aidence and Niramai 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.

Aidence has approximately 1-50 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, Aidence 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.