How Geetha Manjunath built Niramai, an Indian deeptech that screens for breast cancer without radiation

Most second-act founders in Indian tech do something safer than what Geetha Manjunath did. She left a senior research role at Hewlett Packard Labs in Bengaluru, where she had been a Lab Director, to start a medical-device company, a category most of her peers actively avoided because it required clinical evidence, regulatory clearances and patient-grade engineering on top of the ordinary startup risk. Niramai Health Analytix, the company she co-founded in 2016 with Nidhi Mathur, was deliberately built for the hardest possible use case: screening for breast cancer in younger Indian women, where mammography is least reliable and access is thinnest.

The product is now in use across hospitals, diagnostic chains and large employer health programmes; the company has won the Nasscom AI Game Changer recognition, the Indian government’s national startup awards and a long list of international medical-device innovation honours; and Niramai has become one of the most-cited Indian examples of deeptech built for a public-health problem rather than a software margin.

Early life

Geetha Manjunath is a Bengaluru-rooted technologist. Public profile interviews place her family in Karnataka and describe a middle-class upbringing in which education was the central asset of the household. The personally formative fact she returns to in talks is not biographical colour but a clinical one: two close relatives in her extended family were diagnosed with breast cancer, both at stages when outcomes are sharply worse than they would have been with earlier detection. That is the lived context that later made an obscure screening problem feel personal rather than academic.

Education

Manjunath is one of the more credentialed founders in Indian deeptech. She holds a PhD in computer science from the Indian Institute of Science (IISc), Bengaluru, and a master’s in computer science from the National Centre for Software Technology (NCST), Mumbai, with an undergraduate engineering degree in computer science. Her doctoral and post-doctoral research sat in the dense corner of computer science where formal methods meet large-scale data systems, not the kind of CV that the consumer-internet wave of Indian startups tends to produce, and exactly the kind of CV that makes a regulated medical-AI product credible to clinicians and regulators.

Career before Niramai

Before founding Niramai, Manjunath spent more than two decades inside two of India’s most consequential industrial research labs. She began her career as a senior researcher at Tata Consultancy Services’ research arm, working on data and software engineering, before moving to Hewlett Packard Labs (HP Labs) in Bengaluru, where she ultimately led the Data Analytics Lab as a Lab Director. The HP Labs role mattered for two reasons. First, it gave her direct, daily experience of how machine-learning systems are built for industrial-grade reliability, the engineering culture of a corporate research lab is very different from the prototype culture of a startup. Second, it put her inside the leadership chain of one of Bengaluru’s flagship R&D campuses, which is where she watched the gap between published AI research and AI shipped into the real world close, slowly, over roughly fifteen years. By the time she left, she had published peer-reviewed work, mentored hundreds of researchers, and been recognised within HP’s global research network. She was, by any external measure, already a successful person before Niramai existed.

Why she started Niramai

The technical insight underneath Niramai is unusually clean to describe and unusually hard to execute. Mammography, the standard breast-cancer screening test, uses ionising radiation, compresses the breast painfully, and is well-known to be significantly less sensitive in younger women with dense breast tissue, who happen to be the demographic where breast cancer in India is increasingly diagnosed. Many Indian women never get screened at all: the equipment is expensive, requires a dedicated radiology suite, and is concentrated in metropolitan hospitals, leaving smaller towns under-served.

Thermal imaging has been studied as a complementary modality for decades, because cancerous tissue has a slightly different temperature signature from healthy tissue. The historical problem has been signal-to-noise: thermal images contain a lot of physiological variation, and the human eye cannot reliably distinguish the patterns. Manjunath’s thesis, published in peer-reviewed research and embedded in Niramai’s patents, was that modern machine learning could reliably extract the relevant pattern from a thermal image, turning a noisy modality into a clinically useful screening tool. The product she set out to build, branded Thermalytix, is a software-plus-hardware screening system that pairs a calibrated thermal camera with the company’s ML stack to produce an automated screening report. Because the procedure is non-contact, non-invasive and radiation-free, it can be deployed outside a radiology suite, in a hospital outpatient room, a diagnostic chain, or a corporate health camp.

The early struggle

The first wall was credibility. A medical-device startup, founded by software researchers rather than radiologists, claiming to screen for cancer with a method clinicians had historically been sceptical of, is exactly the kind of pitch that gets dismissed in the first meeting. Niramai had to assemble the evidence in the right order: an MVP, then a small clinical study, then a multi-site validation, then peer-reviewed publications, then regulatory clearance pathways. None of that is the way a SaaS company is built, and very little Indian venture capital is structured around the patience that this kind of clinical-evidence cycle requires.

The second wall was regulatory. Niramai had to navigate the CE Mark process in Europe, the US FDA pathway, and the evolving Indian medical-device framework under CDSCO. Each clearance is its own multi-year, document-heavy programme requiring quality-management systems that most software companies have never had to build. The third wall was distribution. Even after the device worked and was cleared, the company still had to convince hospitals and diagnostic chains to integrate a new screening modality into existing patient flows, training their staff and re-pricing the test. None of these are problems that scale by hiring more engineers; they are problems that scale by being patient and disciplined for years.

Funding & revenue

Niramai raised its early capital from a mix of venture and strategic investors known in Indian deeptech. Public reporting and the company’s own announcements have confirmed funding rounds led by pi Ventures (an India-focused deeptech fund), with participation from Ankur Capital, Dream Incubator, Beenext and other investors across seed and Series A stages. The company has also received a string of non-dilutive research and innovation grants, including support tied to its early prototype work. Niramai does not publicly disclose its revenue, and circulating figures from third-party databases are unreliable for a private medical-device company at this stage; the durable, verifiable fact is the customer footprint, hospitals and diagnostic centres across India and a growing list of overseas deployments, rather than a specific top-line number.

Geetha Manjunath’s net worth & ownership

There is no reliable, publicly disclosed net-worth figure for Geetha Manjunath, and any number circulated by third-party estimator sites is speculative. As a co-founder of a venture-funded private company, the bulk of her wealth is illiquid equity in Niramai, its value is set by private rounds rather than a market price, and her personal share has been diluted by successive financings, as is normal. A precise rupee figure for a founder in this position cannot be honestly stated, and is not stated here.

Niramai milestones timeline

• 1990s–2014, Manjunath builds a research career at TCS Research and HP Labs, India, eventually leading the Data Analytics Lab as Lab Director.
• 2016, Co-founds Niramai Health Analytix in Bengaluru with Nidhi Mathur, with a thesis that machine learning can make thermal imaging a clinically useful breast-cancer screening modality.
• 2017–2019, Raises early venture capital, secures patents on the core algorithms, completes multi-site clinical validations across hospitals.
• 2018–2020, Receives regulatory clearances including CE Mark and US FDA registrations for the screening solution; wins national and international innovation awards including the Nasscom AI Game Changer and government-recognised startup honours.
• 2020–present, Expands Thermalytix deployments across Indian diagnostic chains, hospitals, and corporate health programmes, and adds international deployments through partners.

Niramai business model

Niramai sells the Thermalytix screening solution to hospitals, diagnostic chains, government programmes and corporate health vendors on a combination of capital-equipment and per-scan pricing, with the AI-generated screening report as the recurring unit. The model is deliberately not consumer-direct: a screening test has to live inside an existing clinical workflow, with a referring doctor and a follow-up pathway, or it does not get used. Two things make the unit economics work over time. First, the hardware is intentionally low-cost thermal cameras paired with proprietary software, so the marginal cost of each scan is small. Second, because the test is non-invasive and radiation-free, it can be administered by trained technicians in non-radiology settings, which expands the market well beyond the small set of full radiology suites in the country.

Niramai competitors

The honest competitive frame is not other thermal-imaging startups, there are very few at clinical scale, but the established alternatives: mammography, ultrasound and clinical breast examination, each with decades of clinician familiarity and reimbursement codes. Niramai’s pitch is positioning rather than displacement: a complementary screening modality that extends coverage to women and settings where mammography is impractical, expensive or contraindicated. A second class of competition is global medical-AI companies building screening tools on conventional imaging modalities, mammography AI, ultrasound AI, which compete for the same hospital procurement budget. Niramai’s structural advantage in the Indian market is the fit between its modality and the realities of Indian breast-cancer demographics: younger women, denser breast tissue, lower screening penetration, and smaller-town distribution.

Growth strategy

Three deliberate choices define Niramai’s build: stack the clinical evidence first, because a medical-device company that tries to scale before it has multi-site validation simply gets shut down at procurement; design the product around the actual Indian patient demographic rather than retrofitting a Western screening flow, because the entire reason mammography under-serves Indian women is the demographic mismatch; and treat regulatory clearances as a market-access strategy, not paperwork, so the same product can be sold into Europe, the United States and emerging-market public-health programmes from a single core platform. These are not glamorous choices, and they are exactly the ones that turn a deeptech demo into a real medical-device business.

What Geetha Manjunath’s story teaches Indian founders

Two lessons travel forward cleanly. First, deep research credentials are an asset that compounds when the problem is regulated, a PhD founder building a medical device convinces clinicians, regulators and grant committees in a way that an undifferentiated founder cannot, and that compounding is the moat. Second, the right second act is not the safest one. Manjunath left a senior corporate research role at 47 to start the harder version of the problem she had been studying, which is the inverse of the standard career path in Indian tech, and the reason the company exists at all.

Sources & verification

This profile is anchored only in publicly verifiable facts. Key claims are supported by:

• Niramai was co-founded in 2016 by Geetha Manjunath and Nidhi Mathur in Bengaluru, Niramai’s own public materials and consistent Indian business-press coverage (Economic Times, Forbes India, YourStory, The Ken).
• Career at TCS Research and HP Labs India, where she led the Data Analytics Lab, Profile features and interviews across multiple independent outlets; consistent across sources.
• Thermalytix is a thermal-imaging plus machine-learning breast-cancer screening solution with CE Mark and US FDA registrations, Company communications confirmed by independent industry and medical-device coverage.
• Investors include pi Ventures, Ankur Capital, Dream Incubator and Beenext, Public funding announcements and investor portfolio listings.

Frequently asked questions about Geetha Manjunath and Niramai

Who is Geetha Manjunath?

Geetha Manjunath is the co-founder and CEO of Niramai Health Analytix, a Bengaluru-based deeptech medical-device company. She holds a PhD in computer science from the Indian Institute of Science and previously led the Data Analytics Lab at HP Labs India after a long career at TCS Research.

What does Niramai do?

Niramai’s product, Thermalytix, is a software-plus-hardware breast-cancer screening solution that pairs a calibrated thermal camera with proprietary machine-learning algorithms to produce an automated screening report. The test is non-contact, non-invasive and radiation-free, which lets it be deployed in settings where mammography is not practical.

When was Niramai founded and where is it based?

Niramai Health Analytix was founded in 2016 and is headquartered in Bengaluru, India, with deployments across Indian hospitals and diagnostic chains and a growing international footprint through partners.

What is Geetha Manjunath’s net worth?

There is no reliable, publicly disclosed net-worth figure for Geetha Manjunath, and any number circulated by third-party estimator sites is speculative. As a co-founder of a venture-funded private company, the bulk of her wealth is illiquid equity in Niramai, whose value is set by private rounds rather than a market price.

What can founders learn from Niramai?

That deeptech founded by deeply credentialed researchers can convert regulatory and clinical complexity into a moat, provided the company has the patience to stack evidence in the right order, and the discipline to design for the patient demographic it actually serves rather than the one a textbook describes.

More founder journeys: women building India’s health stack · women founders in Bengaluru · women founders in deeptech.