Biology, devices, and intelligence.

What the team has actually catalyzed.

Across IIMA Ventures, C-CAMP, IIT Madras HTIC, Venture Center Pune, and the Bharat Innovation Fund. The book spans all three pillars - therapeutics and bio-manufacturing (biotech), point-of-care and imaging hardware (medtech), and clinical AI (AI-driven health) - and demonstrates the model is operational across the full stack.

Three markets, one founder pool

Pillar one - biotech. The Indian bioeconomy crossed USD 165.7B in 2024 and tracks USD 300B by 2030. Biopharma alone is ~USD 100B. ~10,075 biotech start-ups, up from 50 in 2014. 1,500 new biotech companies founded in 2024. The BIRAC-RDI Fund of ₹2,000 crore institutionalises the next decade alongside ₹733 crore already deployed.

Pillar two - medtech. The Indian medical-devices market is USD 11B in 2024 and tracks USD 50B by 2030, growing ~15% annually. India is ~70% import-dependent today - the localisation opportunity is among the largest in any deep-tech sector. PLI for medical devices has crossed ₹1,206 crore committed across 26 companies; AMTZ and Medical Devices Parks are operational.

Pillar three - AI-driven health. The Indian AI-in-healthcare market is growing 30%+ annually and tracks USD 1.6B by 2027. India already has the largest tele-radiology, AI-imaging, and clinical-decision-support deployment outside the US and China. Tier-2 and tier-3 hospital reach - 700M+ patients - is the data and distribution moat global AI-health players cannot easily replicate.

Cost arbitrage cuts across all three. CRO and CDMO services are 30-40% cheaper than US/Europe; Phase 1-3 trials are ~60% cheaper; device R&D and tooling is 50-70% cheaper. The same talent pool moves between wet-lab, hardware, and software - and the same hospitals serve as customer, clinical site, and data source.

Why the Boston/Bay Area view is wrong now

The standard objection - that Indian healthcare is generics-plus, hardware-poor, and software-only - has been quietly invalidated since 2020 in three different ways, one per pillar.

On biotech, AI inflection has compressed preclinical timelines from 4-6 years to 12-18 months. Generative chemistry and protein-engineering models have shrunk the Boston discovery-capex moat. CDSCO has modernised pathways for biologics, advanced therapies, and gene-and-cell therapies.

On medtech, indigenous design plus PLI plus 510(k) acceleration has produced a category of Indian-founded device companies that ship globally - Forus Health (3nethra ophthalmology, 26 countries), Biosense (point-of-care diagnostics, PerkinElmer acquisition), Niramai (AI breast thermography). FDA clearances awarded to Indian-founded device companies have moved from a trickle to a steady cadence.

On AI-driven health, the country has produced the deepest pool of operational clinical-AI platforms in the emerging-market world - 5C Network (700+ hospitals), Predible Health (acquired by nference), Qure.ai (USD 65M+ raised), SigTuple, Onward Assist. Indian patient diversity is a labelled-data advantage that San Francisco simply cannot manufacture.

Where we actively invest, by pillar

Biotech and therapeutics. AI-first target identification, generative antibody design (Profluent and Cradle lineage), AMR-focused platforms in the Bugworks-adjacent space, cell and gene therapy for Indian disease genetics, precision-fermentation bio-manufacturing, methane-to-protein, 3D-bioprinted tissues, and animal/agri bio. We index toward platform plays over single-asset bets.

MedTech and devices. Point-of-care diagnostics (the Molbio template - Indian-origin platforms that become global category leaders), microfluidic and lab-on-chip biosensors, genomic and infectious-disease testing, devices for India-specific disease burden (tuberculosis, diabetic retinopathy ~77M patients, oral cancer 30% of global burden, maternal-and-neonatal), implantables and ophthalmics, and surgical robotics priced for tier-2 and tier-3 hospitals.

AI-driven health. Clinical AI and decision support, AI medical imaging (radiology, pathology, ophthalmology, cardiology), AI-driven adaptive trial design, multi-omics for Indian populations, ambient clinical documentation, hospital-operations AI, and population-health analytics. We are particularly interested in companies that bind labelled Indian clinical data to a globally exportable AI model.

Cross-pillar plays. The most interesting future companies often sit between two pillars - AI-driven device platforms, AI-discovered therapeutics with companion-diagnostic devices, bioprinted tissues paired with imaging-AI quality control. The seams between the three are where we expect outsized winners.

Lessons we have absorbed about what does not work

Prescription digital therapeutics - Pear and Akili both collapsed in the US. The Indian analogue depends on out-of-pocket spend or under-developed insurance reimbursement and is even harder.

Single-asset therapeutic plays at Series A - even at Indian cost points, moving a single asset through Phase 1, 2, and 3 exceeds what a seed fund can sensibly underwrite. We index toward platform plays.

Generics-plus and biosimilars without genuine technical differentiation - the Indian generics industry is competitive enough that incremental entrants without specific IP do not produce venture returns.

Pure-software AI-health that solves a workflow problem without a clinical end-point or a regulatory path - we have seen this category build revenue but not value. Clinical evidence and reimbursement architecture are non-negotiable.

Why now, structurally

AI inflection has moved discovery from capex-heavy wet-lab to compute-heavy in-silico, and has simultaneously made clinical AI deployable at scale. The BIRAC-RDI Fund (₹2,000 crore), PLI for medical devices (₹3,420 crore notional outlay), and the broader ANRF ₹50,000 crore commitment to translational research institutionalise state capacity across all three pillars at once.

Indian listed-market appetite for biotech, diagnostic, device, and digital-health IPOs is open and widening. Domestic strategic acquirers - Apollo, Max, Tata 1mg, BIOCON, Sun, Cipla - have become serious buyers. Disease-burden imperatives (TB, oral cancer, diabetic retinopathy, AMR, maternal mortality) give India a structural advantage as the first-validation market for globally significant categories.

And the same founder pool now moves fluidly between biology, hardware, and software. The Bangalore-Hyderabad-Chennai-Ahmedabad arc has reached critical mass for cross-disciplinary teams in a way it had not five years ago.

Why full-stack wins in Indian health

Indian healthcare buyers - hospitals, governments, payors, patients - pay for clinical outcomes and patient throughput, not for boxes or molecules in isolation. A pure-technology sale (a device, an assay, a drug, an algorithm) almost always loses pricing power to the layer below that runs the workflow. The companies in our book that have compounded most have all become full-stack operators.

The pattern is consistent across the three pillars. On AI-driven health, 5C Network sells AI tele-radiology as a 24/7 reporting service to 700+ tier-2 and tier-3 hospitals - the hospital pays per report, 5C runs the platform, the radiologists, and the SLA. On medtech, Forus Health deployed its 3nethra ophthalmology platform paired with screening-camp operations across 26 countries; the device is the wedge, the screening service is the business. On biotech and pharma, the strongest portfolio companies own GMP manufacturing, contract development, and direct customer relationships rather than licensing IP and stepping back. Niramai and Qure.ai sell deployment, clinical change-management, and reimbursement support alongside the model.

For Indian founders, the full-stack pattern also unlocks something pure-IP plays cannot - first-validation data from operating the service at scale. That data feeds back into the next product cycle, the next regulatory submission, the next geography. We strongly prefer founders who plan to own the customer, not just the technology.