Niramai vs Insitro

Insitro has a disclosed valuation of $2.2B, while Niramai's valuation is not publicly available, making a direct size comparison difficult. Insitro employs 300 people.

Insitro has raised more in total funding at $743M, compared to Niramai's $17M — a gap of $726M. Combined, the two companies have completed 6 known funding rounds.

Insitro leads with an Awaira Score of 73/100, while Niramai sits at 60/100. That 13-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Niramai was founded by Geetha Manjunath in 2016. Insitro was founded by Daphne Koller in 2018. 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 development and a major Indian public health challenge. Insitro: Insitro is an AI-driven drug discovery company founded in 2018 that applies machine learning to accelerate pharmaceutical development. The company combines computational biology, artificial intelligence, and wet-lab experimentation to identify and validate drug targets more efficiently than traditional methods. Insitro's platform uses proprietary algorithms to analyze complex biological data, predict drug efficacy and safety profiles, and optimize candidate selection across therapeutic areas including metabolic disease, oncology, and immunology. The company has raised $743 million across multiple funding rounds, achieving a valuation of $2.2 billion as of its Series C stage. This positions Insitro among well-capitalized AI healthcare startups addressing the structural inefficiencies in drug discovery. The company partners with pharmaceutical institutions to apply its technology to their pipelines, utilizing both internal discovery programs and collaborations. Insitro's competitive approach differs from pure software platforms by integrating experimental validation, reducing the translation gap between computational predictions and real-world drug performance. The company operates in a crowded but expanding market of AI drug discovery platforms competing against both established pharma AI initiatives and other venture-backed startups. Its growth trajectory reflects investor confidence in AI-enabled drug discovery models, though clinical validation remains ongoing for internally developed candidates. Insitro uniquely combines machine learning with integrated wet-lab capabilities rather than operating as pure software, bridging computational predictions to experimental validation.

Niramai got there first, launching in 2016 — that's 2 years of extra runway. Insitro didn't arrive until 2018. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Niramai has about 50-200 employees; Insitro has about 300. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Niramai and Insitro compete in AI Healthcare, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.