Harrison.ai vs Niramai

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Harrison.ai employs 100-500 people, while Niramai has 50-200 employees.

Harrison.ai has raised more in total funding at $129M, compared to Niramai's $17M — a gap of $112M.

Harrison.ai holds the higher Awaira Score at 68/100, compared to Niramai's 60/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 8-point gap that reflects meaningful differences in scale or traction.

Harrison.ai was founded by Aengus Tran in 2018. Niramai was founded by Geetha Manjunath in 2016. Visit each company's profile on Awaira for a full founder biography.

Harrison.ai: Harrison.ai develops AI radiology and pathology analysis software for clinical deployment, building FDA-cleared and TGA-registered algorithms for chest X-ray abnormality detection, CT pulmonary angiography analysis, and mammography screening under its Annalise.ai product brand. The Sydney company focuses on AI clinical decision support that helps radiologists prioritise worklists, detect abnormalities, and reduce reporting errors in high-volume radiology departments.\n\nThe company raised approximately $129 million including a Series C from investors including Blackbird Ventures, Skip Capital, and Telstra Ventures. Harrison.ai has deployed its Annalise.ai platform across Australian hospital networks and has received US FDA clearance for its chest X-ray AI product, enabling international commercial expansion beyond Australia. The company has published clinical validation studies demonstrating AI performance that is non-inferior to specialist radiologist reads on chest X-ray abnormality detection across multiple institutions.\n\nHarrison.ai competes in the AI radiology market against Aidoc, Lunit, Qure.ai, and Behold.ai, which all target radiologist workflow assistance and clinical alerting. The Australian healthcare market provides a strong home base given the National Health Service framework and centrally coordinated radiology procurement, while FDA clearance opens the substantially larger US radiology AI market. The company is considered one of Australia most promising medical AI companies and a flagship for the Australian healthcare technology ecosystem. 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.

Niramai was founded first in 2016, giving it 2 years of additional market experience. Harrison.ai was founded later in 2018. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Harrison.ai has approximately 100-500 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, Harrison.ai 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.