A data layer for care.
Intelligence built on it.
I’m building a data and AI intelligence layer for clinics, doctors, and hospitals.
More intelligence starts
with more context.
A doctor listens, observes, remembers, and connects the dots. Could AI become useful by working with that same richness of context?
The idea behind Convertase is a data layer that could bring together conversations, observations, records, and operational signals. Then, intelligence over that layer, with the doctor able to see, question, and control what it uses.
Many ways to see.
One layer to understand.
A phone. A conversation. A report. A wearable. The form factor could change; useful clinical context is the common thread.
Context first.
Intelligence over it.
Could natural conversation become a useful, clinician-reviewed record? Tools like Wispr Flow suggest a more fluid way to interact with software; clinical use would need its own validation.
Three contexts.
A shared foundation.
These are starting points for exploration. Each asks what intelligence over better context could make possible.
Could every visit start with the whole story?
Less time reconstructing a patient’s past. More attention for the person in front of you.
- Voice-assisted documentation, reviewed by the doctor
- Patient history that carries context between visits
- A useful record across paper, phones, and clinic systems
India first.
A wider horizon.
India is where I want to begin. The bigger question is how to help healthcare do more with the people, time, and beds it already has.
Over time, I’d like to explore underserved, growing healthcare markets such as the Philippines—and work with hospitals elsewhere on bringing their data and workflows together.
Different pathways.
One shared intention.
The name comes from biology. Three complement activation pathways converge on C3-convertase activity. Different routes, a shared process.
That is the idea I’m borrowing: clinics, doctors, and hospitals could be connected by a common data layer, each benefiting in a different way.
Read the philosophyNotes from
the builder phase.
All notes The model is only part of the story.
Why I’m starting with the information around care, and the intelligence that could grow over it.
Could the beds we have go further?
An opening question about hospital capacity, and why it brought me to AI in healthcare.
Why three pathways?
The biology behind Convertase, and three ways to explore one shared foundation.
Hi, I’m Aayush.
An AI engineer with an MBA lens.
A builder, always.
My path runs through IIIT Nagpur and IIM Mumbai. Coming from a family of doctors, healthcare is personal. As India’s AI ambitions grow, I keep coming back to a question: could we get more care from the beds and resources we already have?
A little more about meWhere things
stand today.
What exists today?
Convertase is an early exploration. This website shares the idea, questions, and directions I want to investigate. There is no clinical product, live integration, or validated performance claim being offered here.
Why begin with a data layer?
My starting hypothesis is that useful intelligence depends on useful context. A model may need the conversation, the relevant history, the observation, and the operational picture together. I want to investigate how to bring that context into a form clinicians can inspect and control.
Are the devices and integrations confirmed?
No. Phones, smart glasses, wearables, edge/IoT, and voice tools are possible form factors. Mentions of Meta or Wispr Flow are references to existing technologies, not announcements of partnerships or integrations.
Where would this begin?
India is the starting point. I’m interested in other underserved, growing healthcare markets, including the Philippines, and in learning from hospital workflows elsewhere. The initial setting and first product are still open.
Who would stay in control?
Clinician control is a founding design principle. I want to explore systems in which a doctor can inspect the source, correct the context, assess uncertainty, and decide what is used. Privacy, consent, and clinical evaluation would need to shape any implementation.
The next useful insight
might come from you.
If you work in care, run a hospital, or are asking similar questions, I’d value your perspective.
Compare notes with Aayush A conversation, at the very beginning.