Verified Clinical Data Marketplace for Healthcare AI Training
A “data company” that investors twice passed on over regulatory risk became compliance infrastructure with a documented DPDP moat, a term sheet in 11 weeks, and a Wellcome Trust grant.
- Origin
- Joint research initiative between AIIMS clinical faculty and an IIT computer science department
- Founded
- 2022
- Core Innovation
- B2B SaaS: verified, anonymised clinical data licensed to healthcare AI laboratories, with a three-layer verification protocol commanding premium pricing.
- Capital Outcome
- ₹9.40 Crore total: ₹2.20 Crore Wellcome Trust Data for Health grant and ₹7.20 Crore seed equity from a HealthTech-focused early-stage fund
Diagnostic findings at engagement
- The company described itself in all materials as “a data company,” a positioning that triggers immediate investor concern around data ethics, governance liability, and long-term defensibility regardless of underlying technical merit.
- No architecture existed for compliance with the Digital Personal Data Protection Act 2023, a gap investors identified as a regulatory and legal liability during initial diligence conversations.
- The revenue model consisted of per-dataset, one-time licensing transactions with no recurring revenue mechanism, producing unit economics resembling a data brokerage rather than a software business.
- Two early-stage venture pitches had resulted in passes, both citing an unclear regulatory moat as the principal reason.
- Applications to the Indian Council of Medical Research and the Department of Biotechnology had been rejected on the basis that the underlying activity was assessed as commercially, rather than research, motivated.
Service interventions applied
Business Model Restructuring
Redesigned the revenue architecture from one-time dataset licensing to annual subscription-based API access with tiered pricing, supplemented by data annotation and model validation services as additional recurring revenue lines. Gross margin improved from 38% to 71%.
Regulatory Architecture
Designed a Digital Personal Data Protection Act 2023 compliance framework comprising a consent management system, a data fiduciary registration roadmap, and an anonymisation protocol audit, converting the company's regulatory exposure from an investor-cited liability into a documented compliance position competitors had not yet replicated.
Investor Narrative Reframe
Repositioned the company from “data company” to “the compliance and quality verification infrastructure layer required by every Indian healthcare AI company.” The reframed positioning secured two qualified investor meetings within 30 days.
Grant Narrative: Wellcome Trust
Built the Data for Health application around a distinct public health rationale: that AI diagnostic models trained predominantly on Western patient data systematically underperform on Indian disease presentation patterns, and verified India-specific data infrastructure is a public good prerequisite for clinically reliable AI deployment.
Dual Pitch Deck Construction
Built separate documents for grant and venture audiences rather than a single hybrid pitch. The grant document emphasised public health impact exclusively; the venture document opened with market sizing (an estimated ₹9,400 Crore Indian healthcare AI market by 2028), competitive moat duration, and the restructured ARR model.
Capitalisation Table Management
Structured the seed round using clean priced equity rather than convertible instruments, to avoid complicating subsequent institutional diligence. Established a 12% ESOP pool and negotiated clean equity terms for the AIIMS and IIT institutional stakes that had previously existed only as informal arrangements.
Observation: Dual-Audience Narrative Construction
A single company frequently requires two structurally distinct narratives depending on whether the audience is a grant-making institution funding public goods or a venture investor underwriting a defensible, recurring-revenue business. A document constructed to satisfy both audiences typically satisfies neither. In this engagement, the grant application made no reference to annual recurring revenue; the venture pitch made no reference to disability-adjusted life years. Both were independently successful.
Position at engagement vs. position at close
Swipe the table sideways to compare.
| Dimension | At engagement | At close |
|---|---|---|
| Revenue Model | Per-dataset licensing, non-recurring | Annual subscription plus services, recurring |
| Gross Margin | 38% | 71% |
| Regulatory Position | DPDP non-compliant; cited as investor red flag | DPDP compliance framework documented; cited as investor moat |
| Venture Outcome | Two passes, “unclear regulatory moat” | Term sheet within 11 weeks of repositioning |
| Capital Secured | ₹0 | ₹9.40 Crore |