Skrypt Health is not a software company that noticed healthcare had a gap. It was built by someone who grew up inside clinical environments, studied both medicine and computer science, and spent years validating the problem firsthand — walking into dental, veterinary, medical, and pharmacy practices before writing a line of production code.
Hasan started working in clinical environments as a teenager, which gave him an early and direct view of the operational gap between what clinical software promised and what actually happened at the front desk. In high school he published research in digital imaging in hospital settings — an early signal of where his work was heading.
At Western University he studied Medical Sciences and Computer Science simultaneously, and during that time published machine learning research applied to clinical contexts. He went on to spend nearly half a decade at Procter & Gamble working in operations and data science, developing the analytical and systems-design skills that now underpin Skrypt's architecture.
Skrypt started with a deliberate, unglamorous process: walking into dental, veterinary, and medical clinics across downtown Toronto to interview front desk staff and practice owners about what actually broke down during the call queue. The MVP was built from those conversations. What you see today is the result of that ground-level research compounding over time through real customer deployments.
A small team building AI for the front of every clinic.
Toronto · Houston · Est. 2018.
Most AI tools for healthcare are general-purpose software with a HIPAA checkbox added later. The integration layer is typically a bolt-on. The compliance posture is an afterthought. The call workflow logic was designed for call centers, not clinical front desks.
Skrypt was built the other way around. The compliance framework came first. The PMS integrations — Open Dental, ABELDent, Dentrix, Eaglesoft, AVImark, Cornerstone, IDEXX — were built before the marketing page. The call workflow logic was designed around the specific patterns of dental, veterinary, and medical inbound calls, not generic customer service queues.
That specificity is the product. A dental practice owner should not have to explain to their software what a recall appointment is. A pharmacy should not have to explain what a prescription refill queue looks like.
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