Niramai vs Regard AI

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

Regard AI has raised more in total funding at $30M, compared to Niramai's $17M — a gap of $13M.

Niramai holds the higher Awaira Score at 60/100, compared to Regard AI's 55/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 5-point gap that reflects meaningful differences in scale or traction.

Niramai was founded by Geetha Manjunath in 2016. Regard AI's founder information is not currently available in our database.

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. Regard AI: Regard AI builds an AI clinical documentation assistant that integrates with hospital electronic health record systems to automatically identify potential diagnoses, surface relevant clinical evidence from patient records, and generate structured documentation for physician review. The system is designed to reduce the documentation burden on hospitalists and clinical teams while improving diagnostic completeness and coding accuracy.\n\nThe company raised approximately 30 million USD and has deployed across hospital systems focused on reducing physician administrative load, a problem that contributes to clinician burnout and documentation errors with downstream billing and quality implications. Regard operates as a background analysis layer that surfaces findings without requiring clinicians to change their existing EHR workflow.\n\nClinical documentation AI sits at the intersection of physician workflow optimization and revenue cycle management, giving it a dual value proposition that justifies enterprise procurement from both clinical operations and hospital finance departments. Regard competes with Nuance DAX, Abridge, and other ambient documentation tools, but its focus on diagnostic suggestion and coding completeness rather than pure transcription differentiates its clinical and financial value story.

Niramai was founded first in 2016, giving it 1 year of additional market experience. Regard AI was founded later in 2017. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Both Niramai and Regard AI report similar employee counts of approximately 50-200. Team size is often a proxy for operational scale, though lean AI companies can punch well above their headcount.

Yes, Niramai and Regard 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.