Beyond the Transaction: Building Healthcare’s Connected Record

India’s health-tech sector is crowded with platforms competing on downloads, city coverage, and network size metrics that, as Gaurav Dubey, Founder & CEO of LivLong 365, argues, any well-funded rival can replicate within a year or two. The real differentiator, in his view, lies elsewhere: in architectural decisions made years before the growth curve demanded them, and in treating a person’s insurance, consultations, diagnostics, and claims as a single continuous record rather than four disconnected silos. In this conversation, following 273% year-on-year growth in profit before tax, Dubey unpacks the infrastructure choices behind that scale, why India’s health data fragmentation is an incentive problem rather than a technical one, and what it actually takes to turn diagnostics from a commodity transaction into genuine decision support.

CISO Forum: LivLong 365 grew 273% in PBT year-on-year; that kind of growth puts enormous pressure on technology infrastructure. What were the one or two architectural decisions you made early that allowed the platform to scale without breaking, and what would you do differently?

Gaurav Dubey: That kind of growth curve is exactly the scenario you have to architect for in advance, because by the time the traffic graphs show a problem, you’re already behind. In our case, we made two decisions very early on, and those decisions have paid off extraordinarily well.

The first decision we made was to approach the creation of our Payvidor model, integrating payer systems with provider systems, as a series of independent service components rather than as a single monolithic service structure. Insurance systems have an entirely different load profile than claims systems; outpatient consultation services have an entirely different load profile than any other service component, as do diagnostics. The decoupling of service components enabled a surge in lab appointments without putting claim processing at risk; therefore, we were able to continue adding verticals like SME and the labs marketplace without repeatedly re-architecting our core Payvidor component.

The second decision we made was to build our data architecture to treat every user’s insurance, consultation, diagnostic, and claims history as a single connected record rather than four separate ones. That decision surely has paid off as of late; we would have been forced to endure a significant, costly reengineering effort had we not made it ahead of time, given how quickly user expectations around a single, coherent health record have evolved.

One thing we would do differently is to conduct an extensive observability effort across the payer and provider seams simultaneously, rather than as a second wave, while building the core systems. Every other aspect of the early architecture has held up exactly as planned; we would have compressed the construction timeline for that phase.

2. India generates an extraordinary volume of health data, but most of it sits in silos across hospitals, labs, insurers, and wearables with no real interoperability. What is the actual infrastructure barrier to fixing that, and who needs to move first?

Gaurav Dubey: The honest answer is that the barrier isn’t technical; it’s incentive-driven. Every hospital, lab, insurer, and wearable maker has its own reason to hold on to its slice of a person’s health data, and none of them is under real pressure to hand the complete picture back to the person it belongs to. So the person ends up doing the integration work themselves, carrying reports between doctors, re-explaining their history at every new provider, hoping someone remembers the test from eighteen months ago.

That’s the gap we built LivLong 365 to close. Insurers, in particular, are structurally not built to solve this; their systems are designed around a claim or a policy, not around a person’s ongoing health journey, so even when they hold years of a customer’s data, they can’t easily turn it into something useful for that customer day to day. We took the opposite starting point: build one system where a person’s insurance, consultations, diagnostics and claims live together, so what comes back to them isn’t a stack of disconnected reports but a usable, ongoing picture of their own health.

Who needs to move first is really whoever is closest to the user relationship and has the least to lose by connecting the dots; that’s where platforms like ours have an advantage insurers don’t, because our incentive is aligned with giving people the connected view they actually want, not with protecting a data silo.

3. Preventive healthcare is easy to talk about but hard to operationalise at scale. At LivLong 365, where exactly does the technology intervene and at what point does personalisation become genuinely predictive rather than just reactive with better branding?

Gaurav Dubey: Most people think that what they are receiving is Customised preventive care, but it is actually just a way of adding people into groups based on some characteristic, like their age or medical history and giving them a small, more relevant nudge to take some action. This is helpful, but it still doesn’t predict when something might happen.

Our product provides predictions based on how you are using your data to improve your experience before you would recognise the need for improvement, rather than on your history with that same data. Your experience will be personalised in real time based on this data. We’ll continue to gather data until we have enough for all users to provide predictions. You can classify our chronic care cohorts as the furthest along on this path.

4. You describe the diagnostic layer as where the real intelligence lives. Most health-tech platforms treat diagnostics as a commodity, a transaction to be made cheaper and faster. What are they missing, and what does it look like when diagnostics actually become a decision-support layer?

Gaurav Dubey: Most platforms treat diagnostics like an entity unto themselves that only exists at the point of transaction, and even though a result is not a final disposition but rather a beginning.

The larger issue is structural. Traditionally, diagnostics are siloed from consults & claims, so when you get a digital result, it is still just a static report that requires manual reading and action by the recipient. No one has built anything capable of creating the next step.

For decision support, results are interpreted in context, flagging & tracking prior results on the trend; then directing to the next step. This means that diagnostic, consult & claims data must exist in a single system rather than multiple systems, and that workflows must guide moving forward rather than leaving a report in an inbox. This work, though, is often harder and less visible, which is why most platforms focus only on expediting transactions.

5. Cybersecurity in healthcare is chronically underinvested relative to the sensitivity of the data involved. As CEO of a platform handling health records for individuals and SMEs across 800 cities, what keeps you up at night and what does a genuinely robust security posture look like for a company at your stage of growth?

Gaurav Dubey: We’re not one system; we’re insurance, consultations, diagnostics, and claims all talking to each other, often through partners and third-party labs we don’t fully control end-to-end. Each integration point is a place where a strong posture on our side can still be undermined by a weaker link on someone else’s.

A genuinely robust posture at our stage isn’t about having the most tools; it’s about treating security as continuous rather than a project you finish. That means encryption and access controls as defaults, not add-ons; regular third-party audits rather than self-certification; and assuming breach scenarios in how we design systems, not just in how we respond to them. Scale without that discipline is exactly how healthcare data ends up in a breach headline; the ambition to grow fast has to be matched by an equal, unglamorous discipline around what happens at every seam in the system.

6. SMEs are an unusual healthcare constituency, typically underserved, cost-sensitive, and without the HR infrastructure that large enterprises use to manage employee wellness. What does it take to build a technology product that actually works for that segment, rather than a scaled-down version of something designed for someone else?

Gaurav Dubey: The assumption that SMEs require a scaled-down version of an enterprise health and wellness program is incorrect; they simply require a solution that does not assume there will be an HR department responsible for implementation. A large enterprise likely has benefits managers who facilitate the hiring process, claims assistance, and the contract & relationship with their provider. In contrast, a 40-employee SME does not have any of these in place.

The product question is not what capabilities do I remove, but what can function autonomously. Therefore, any capability that exists, such as enrollment processes, claims, and the timing/frequency of OPD visits, must operate with almost no administrative workload for the employer’s operations; the business owner should not need to be a benefit administrator to provide this to their employees and participants. The cost structure has as much to do with this as the technology does; only when you create a flexible and scalable benefit delivery platform will you be able to create a pricing structure allowing for a company of 20 employees to deliver benefits as efficiently as a company with 2,000+ employees.

When you build the benefit delivery platform with the intent of solving this problem, it is no longer designed for small business enterprises; rather, it is a distinctly different design challenge created to assist an individual who has never received a comprehensive health benefits package.

7. India’s health-tech space is crowded with well-funded platforms competing on the same metrics — network size, city coverage, app downloads. What is the technology bet LivLong 365 is making over the next three years that most of its competitors are not, and where do you think the real moat in this space will ultimately be built?

Gaurav Dubey: Typically, platforms differentiate themselves by acquisition metrics, cities onboarded, app downloads, and network size. While necessary, none of these provides distinct value from a moat perspective, since any well-resourced competitor can replicate the same numbers through spend or acquisitions within a year or two.

We believe that the definitive moat is built on the depth of data accumulated about each user and the ability to turn that data into something genuinely useful for them, not on the sheer size of a platform’s distribution footprint. A comprehensive, longitudinal account of a person’s healthcare journey insurance, consultations, diagnostics, and claims—built over years rather than a single transaction, is exactly what insurers have the raw material for but rarely deliver back to the customer. That gap is our opportunity, and it’s why our data architecture was built to close it well before it became a competitive talking point.