The Intelligence Layer: Why India’s Healthcare AI Moat is Built on ABDM Rails

The future of India’s healthcare AI moment will be defined by the systems that deeply integrate into Indian clinical realities. This was the takeaway from the Beyond Automation fireside chat in Bengaluru, hosted by Databricks for Startups, eka.care, and 3one4 Capital on 20th May, 2026.

Moderated by Dilip Krishna of Databricks, the discussion featured Vikalp Sahni, Founder and CEO of eka.care, and Ayushi Garg, Principal, 3one4 Capital. Together, they dissected the shift from generic AI optimism toward clinical-grade AI, which is a standard defined by consent-native infrastructure and proprietary, compounding data.
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Infrastructure as the Catalyst

Healthcare AI in India is currently hitting a unique inflection point driven by the Ayushman Bharat Digital Mission (ABDM). While Western healthcare remains trapped in fragmented, proprietary silos, India is constructing a public digital rail that prioritizes interoperability at a population scale. Through ABHA IDs and consent-based data exchange, the industry is moving toward a reality where longitudinal patient data is finally portable and operationally usable.

Unlike previous generations of healthcare software that operated inside isolated hospital databases, AI-native products in India can now access a continuous stream of consented patient data across systems.

Vikalp Sahni noted that the primary realization during the pandemic was that while India had successfully digitized payments and banking infrastructure, healthcare data remained fragmented and inaccessible. ABDM effectively changes that equation. Consequently, the healthcare companies that dominate the next decade are unlikely to be standalone applications. Instead, they will be infrastructure-native products that treat the ABDM ecosystem as their foundational layer.
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Defining Clinical-Grade Beyond the Hype

Clinical-grade is often reduced to a marketing buzzword, yet in practice, it represents a rigorous localization of AI that frontier models cannot achieve out of the box. True clinical-grade AI in India requires much more than plugging a model into a medical workflow. It demands an understanding of specific Indian clinical protocols and local prescribing behaviors, specifically where branded medication names often take precedence over generic pharmaceutical terms.

These AI systems must also navigate the specific workflows of Indian hospitals and the unique language patterns of Indian patients. Lacking these contextual layers, even the most powerful models fail to deliver reliable outcomes for clinicians. This competitive moat can be established by the sophisticated system of localized tooling, workflow orchestration, and proprietary healthcare ontologies built around the underlying model.
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The Investor’s Lens: Where Defensibility Lives

From an investment perspective, distinguishing durable businesses from transient AI applications remains a primary challenge at the early stage. Ayushi Garg observed that while healthcare has become one of the largest categories evaluated at 3one4 Capital, the era of simple wrappers has reached its limit. The easiest companies to filter out are those that function as thin interfaces on top of third-party APIs without owning the underlying workflow or distribution.

Durable defensibility in this sector emerges through the compounding of value over time. This compounding typically manifests through deep workflow integration, embedded clinician behavior, and the mastery of regulatory and compliance infrastructure. Healthcare AI products become exponentially stronger as they become more integrated. While point solutions may drive short-term adoption, AI-native healthcare systems will eventually converge into full-stack workflows that own the clinical interaction end-to-end. In a regulated sector where accuracy and auditability are as vital as user experience, this integration is the only path to long-term survival.
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From Demos to Production Scale

The transition from a successful AI demo to a production-scale healthcare deployment is a hurdle of governance rather than just engineering. Databricks framed this challenge through the lens of data architecture. Modern healthcare organizations operate across a chaotic landscape of imaging platforms, claims systems, medical PDFs, and disconnected databases. The real challenge is not merely building a model to read this data, but creating governed, interoperable systems where data pipelines, AI orchestration, and cost controls operate in unison.

In an environment where model hallucinations carry real-world consequences and patient privacy is non-negotiable, the infrastructure layer becomes the ultimate safeguard. Success requires creating an environment where data governance is mandatory and clinical accuracy thresholds are significantly higher than in any other industry. India’s structural advantage lies in the fact that it may not need to copy Western healthcare models. By building on a foundation of open protocols and portable identity systems, India is positioned to create an entirely new healthcare intelligence layer specifically designed for its unique demographic and operational realities.

The conversation ultimately reinforced a broader shift: the strongest AI businesses are built where proprietary workflows, data loops, and infrastructure-level integration exist simultaneously. Healthcare in India now possesses all three, signaling that the next wave of winners will be those who solve the hard problems of trust, regulation, and deep institutional integration.

3one4 Capital remains committed to backing the practitioners who understand that in healthcare, specificity is the only path to scale.

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