Causaly vs Niramai

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

Niramai has raised $17M in disclosed funding across 0 known rounds. Causaly's funding history is not publicly available.

Niramai holds the higher Awaira Score at 60/100, compared to Causaly'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.

Causaly was founded by Elias Iosif in 2018. Niramai was founded by Geetha Manjunath in 2016. Visit each company's profile on Awaira for a full founder biography.

Causaly: Causaly built a biomedical AI platform that extracts and maps causal relationships from scientific literature at scale, allowing pharmaceutical researchers and scientists to query cause-and-effect relationships across millions of published papers and clinical documents. The platform used a specialised natural language processing system trained to identify causal assertions in biomedical text, producing a structured knowledge graph of disease mechanisms, drug targets, and biological pathways.\n\nThe company was founded in London in 2018 and was acquired by Elsevier, the academic publishing giant, in 2022. The acquisition gave Elsevier an AI layer to sit atop its vast corpus of scientific publications, enabling researchers using ScienceDirect and other Elsevier products to query causal biological knowledge rather than simply searching for documents. Prior to acquisition, Causaly had built a client base among pharmaceutical R&D teams seeking to accelerate literature review and hypothesis generation.\n\nCausaly represented a niche but strategically important segment of the biomedical AI market: causal reasoning and knowledge graph construction. Its acquisition by Elsevier followed a broader trend of academic publishers acquiring AI startups to enhance their platforms, similar to Wiley acquiring Atypon and Springer Nature building AI discovery tools. The company is now integrated into Elsevier Research Intelligence, extending causal AI capabilities to Elsevier institutional subscribers worldwide. 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. Causaly 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.

Causaly 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, Causaly 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.