Two Engineers, One Phone Number and the Birth of Pype AI 

In early 2024, as the world cautiously emerged from COVID’s long shadow, two engineers began asking a simple question: Why is the healthcare system built for systems, not for people? For Dhruv Mehra, cofounder and CEO of Pype AI and a former Meta engineer, the pandemic had reset something fundamental. 

“I started taking my health seriously,” he explained. “And when you start pursuing something in that direction, you start exploring what else is there.” That curiosity, mixed with his belief that large language models could solve problems beyond prototypes, brought him back to India and into the maze of healthcare.

Mehra arrived in India in 2023 with an agency-style idea: build AI applications for companies, explore use cases, and see what sticks. He spent months developing small LLM tools, often calling his long-time friend Ashish Tripathy, who worked as a senior data scientist at LinkedIn and contributed to its “write with AI” feature.

Their conversation, which was technical at first, slowly drifted toward a shared understanding that healthcare was full of unmet needs hiding in plain sight. 

A Single Phone Number That Changed Everything

The breakthrough came with one phone number. At HCG, oncologist Dr Vishal Rao had a contact line on Google and Instagram that cancer patients called to enquire about a low-cost prosthetic device for throat cancer. The volume was overwhelming. Neither Rao nor his personal assistant could keep up. 

Mehra and Tripathy offered to listen in. “We actually attended and tried to listen to those calls,” Tripathy said. Over time, the prototype they built became multilingual, automatically recorded calls, generated summaries and shared them daily with hospital staff. What began as a patch for a broken workflow became the foundation of Pype AI.

Inside the Operational Mess Hospitals Hide

With the early success, the founders assumed hospitals would be eager for more automation. They were wrong. Their first product leaned on evaluation-focused AI tooling, which hospitals had little interest in. Most distrusted the autonomous agents.

“Healthcare being a very regulated industry… wherever we went, people said, I don’t trust the agents,” Tripathy said. So the founders did something unscalable. They moved into hospitals.  

At Sparsh and HCG in Bengaluru, they spent weeks sitting beside call operators, clinicians and front-desk staff. They watched operators handle a never-ending stream of patient queries while juggling Excel sheets carrying discount rules, doctor schedules, branch-specific offers and quirky internal protocols. 

Some nuances were absurd. A discount on a specific health package might apply only at one branch. Doctors had their own parity system dictating who received new OPD patients. Those rules existed only in operators’ heads. Staff attrition made it even messier. Operators sometimes quit without notice, and onboarding replacements took months.

The founders learnt that frontline hospital communication was not clinical work. It was context recall, triage, schedule navigation, emotional labour and quick decision-making, all built on scattered information. No app or chatbot had ever come close to capturing that complexity.

From Appointment Bots to Care Coordinators

These observations pushed the team to reposition the product. Instead of building an appointment bot, they began designing a care coordinator. The agent needed to understand multi-speciality departments, doctors practising in multiple facilities and the constant churn of operational rules. 

“It is intelligent enough to understand if a doctor is working at multiple facilities,” Mehra said.

The system soon expanded to handle conversations in Kannada, Telugu, Tamil and Hindi. It built a living glossary of medical terms and hospital-specific shorthand. 

A breakthrough came when doctors began correcting the agent during test calls. By saying “feedback,” clinicians could switch the agent into a correction mode and note inaccurate terminology. “They can say the agent used a wrong term… feed it into our feedback loop,” Tripathy said. 

These corrections helped refine prompts and terminology libraries.

The work began changing the founders, too. Mehra recalled watching patients travel long distances for simple queries or paperwork. “These websites are designed for English-speaking audiences,” he said. Many patients did not want another app. They wanted someone who would pick up the phone.

Manual Testing Still Matters

This feedback-centred evolution grew into the core of Pype’s research philosophy. Many competitors, Mehra argued, lean too heavily on AI judging AI, LLM-as-evaluator pipelines, ignoring the nuances of healthcare conversations. “If we have built something foundationally solid, we have to go via manual testing,” he said. 

So Pype put nurses, doctors and frontline staff at the centre of its testing loop. They challenged the agent on tone, context, empathy and clarity, areas where automation often fails. “Another AI agent can’t do it,” he added. 

Today, the company focuses on chronic and speciality care, particularly oncology, cardiology and orthopaedics. Chronic conditions, unlike surgery-centred workflows, require longitudinal relationships. Pype agents check in for months or years, collecting post-treatment quality-of-life data that hospitals historically lacked the bandwidth to capture. “Our agent checks with them, is there pain? Are they feeling lethargic?” Mehra said. “This data is fueling research like never before.”

To meet compliance requirements, Pype is HIPAA and SOC 2 compliant, encrypts or redacts PHI, and signs strict Business Associate Agreements wherever required. “Hospitals don’t even talk to you unless you have these things in place,” Mehra said. In many deployments, the agent never exports patient identifiers from the hospital’s ecosystem.

Commercial Models and Global Ambitions

For smaller hospitals and speciality departments, Pype follows a SaaS-plus-usage model. Large enterprises license their voice AI for private cloud environments on annual contracts, similar to enterprise tools like Tableau or Mixpanel.

Its investments are divided between research precision and compliance work, each receiving roughly 30-40% of capital, while the rest goes toward team and product development.

The company has raised $1.2 million in pre-seed funding led by Kalaari Capital, with participation from Wyser Capital and Tenity. Its next chapter is shaped around two goals: expanding into the US market, where care coordination is even more fragmented, and building a research footprint strong enough to make onboarding prescriptive rather than exploratory. Over the next two to three years, Mehra said the team is focused on “building credibility in healthcare journals”.

The First Line of Healthcare, Not a Replacement For It

The founders are clear about what Pype AI is not. It is not trying to replace healthcare workers or build autonomous clinical systems. Instead, it wants to become the consistent, reliable first response — the layer that answers before a human can. “We see ourselves sitting there with them,” Mehra said, referring to call centres and care coordinators. “Acting as the first layer of response.”

Pype AI began with two engineers searching for meaning in their own health journeys. What they found instead was a vast population of patients who did not want apps, forms or portals. They wanted someone to listen. And sometimes, Mehra said, that is all the system needs to do: answer when someone calls.

The post Two Engineers, One Phone Number and the Birth of Pype AI  appeared first on Analytics India Magazine.