Our Journey: From Deep Tech Innovation to Global Impact
Niramai was founded in 2016 by Dr. Geetha Manjunath with a clear vision:
to revolutionize breast health through science-backed, accessible, and non-invasive technology.
What started as a research-driven initiative in Bangalore evolved into one of the world’s leading companies in AI-based medical diagnostics. Disturbed by the rising number of breast cancer cases being detected too late — especially in young women — the founding team set out to create a safer, affordable, and more scalable solution.
After years of R&D, clinical trials, and validation with leading hospitals, Thermalytix® was born — an AI-powered assessment tool that uses thermal imaging to detect breast abnormalities without radiation, contact, or pain. The technology is protected by 39 international patents and has been recognized globally, including by the Bill & Melinda Gates Foundation, World Bank, and top scientific publications like The Lancet Oncology and JCO Global Oncology.
We believe every woman deserves access to safe, private, and early breast health assessments — no matter her age, background, or location.
Why Thermalytix Is a Smarter Approach Than Manual Thermography
Traditional thermography relies heavily on human interpretation of color images, which can be inconsistent and subjective. Thermalytix solves three key limitations — with precision and objectivity.
🧩 1. Image Standardization
Manual thermography can suffer from inconsistent images due to cooling, blur, or poor angle.
Thermalytix automatically checks for imaging errors, ensuring reliable inputs every time.
🌡️ 2. Temperature Precision
Color-based thermograms often lose accuracy due to limited color palettes.
Thermalytix uses raw temperature data, capturing even subtle changes in heat — which can matter.
đź“Š 3. Quantitative Analysis
Instead of visual guesses, our AI analyzes vascular, regional, and areolar patterns using quantifiable thermal features. The result? A report indicating the likelihood of malignancy, grounded in data — not human guesswork.