Two dental clinics can sit a few kilometres apart, see roughly the same number of patients, employ dentists of comparable skill, and run on nearly identical software, and still post wildly different numbers at the end of the month. Most owners chalk the gap up to something unmeasurable, a dentist’s chairside manner, maybe, or a front-desk team that’s simply better at follow-up calls.
The real answer, more often than not, is buried inside data nobody in healthcare has been able to read. A single clinic can run six or seven separate systems for billing, scheduling, phones, patient management, marketing and clinical documentation, and still be unable to answer simple questions about its own business, like why one location is growing faster than another, or why patients are declining treatment they already agreed to in the chair. Every system understands its own transaction. None of them understand the full story.
This is the gap that led Shivam Bhatia to found Clinsight AI. Under his leadership as Founder and CEO, the company set out to build a system that connects provider behaviour, patient interactions and revenue into a single, continuous understanding of how a clinic runs, rather than another dashboard recording what already happened.
“The fundamental gap in clinic software is that every product only understands one small part of the business,” explains Bhatia. “One system knows a patient booked an appointment. Another knows they called. Another knows what procedure was completed. Almost none of them understand the full story.”
A Path Shaped by Three Companies
Bhatia didn’t arrive at that idea directly. During the pandemic, he started a Discord community for teenagers interested in coding and startups. It grew past 20,000 members and eventually became SlingshotAhead, backed by Techstars and Alchemist. Sorting through thousands of young builders taught him something that stuck about hidden signals, followed by a stint in fintech at SlashPay and then BharatPe, where he learned that a technically brilliant product counts for little if it doesn’t change how people actually behave.
The pieces came together at LunaBill, where he used AI to automate medical billing and insurance workflows and, working closely with clinics, kept running into the same pattern of fragmentation described above.
“A résumé might tell you where somebody went to school,” Bhatia says, “but it tells you very little about how curious they are, how quickly they learn, or what they’re capable of becoming.”
What Founders Get Wrong About Adoption
That instinct for hidden signals was sharpened by an unlikely conversation with Edward Zuckerberg, a dentist who has spent decades as an early adopter of practice technology. He walked Bhatia through years of tools that were technically impressive and still ended up gathering dust in clinics, because nobody had translated what they did into an obvious Monday-morning benefit for the dentist using them.
“Being technologically better is not enough,” Bhatia says. “If people don’t understand the value immediately, or it doesn’t fit naturally into how they already work, adoption becomes incredibly difficult.”
From Isolated Systems to One Continuous Story
A patient’s relationship with a clinic rarely happens in one step. They discover the practice, call the front desk, book an appointment, hear a treatment plan, sometimes leave without scheduling it, receive a follow-up, and either return or disappear. Every step produces a signal, but most of that signal has historically lived in conversations and notes that no software was built to read.
“You can now connect what was said in a conversation to what happened afterward,” says Bhatia. “That’s very different from a dashboard that just tells you something went wrong.”
Built to Explain the Number, Not Just Report It
Revenue makes the gap easiest to see. Every clinic can tell when production falls 15% in a month. Almost none can say, without pulling reports from five different systems, whether it happened because fewer new patients walked in, calls stopped converting into bookings, or one provider’s approach to presenting treatment quietly changed.
“There’s a huge difference between information and intelligence,” Bhatia says. “The number at the end tells you almost nothing about the chain of events that produced it.”
Starting With Dental, By Design
Clinsight’s first market is dental, and deliberately so. High-stakes treatment decisions, multi-location operators and an unusually high volume of patient conversation make the gap between information and intelligence sharper there than in most corners of healthcare.
“I’d much rather go extremely deep in one market and earn the right to expand than call ourselves a platform for every healthcare business from day one,” Bhatia says.
A Pattern That Extends Beyond Dentistry
Bhatia doesn’t see the underlying problem as unique to dental. Dermatology clinics, med spas, optometry groups and physical therapy practices carry many of the same characteristics: high-value patient decisions, fragmented software, and a business built on conversations that mostly go unread. Dental is the starting point Clinsight has chosen to go deep in first, not the ceiling on what it’s built to solve.
Toward an AI Operating Layer for Every Clinic
Clinsight’s longer-term ambition is to become the AI operating layer for clinics, the one system that understands appointments, payments, conversations and clinical records together instead of as scattered entries across separate tools. An operator running fifty or a hundred locations should eventually be able to ask what changed this week, or which patient conversations the team should follow up on, and get an answer instead of a day of pulling reports.
For two decades, most business software has done one thing well: record what happened. Clinsight is built on the bet that the next generation of that software should be able to explain why.
