u/Rajxai

A few years ago, we were spending a ton of time trying to get healthcare systems to talk to each other.

Different systems, data, formats, and rules.

The goal was basically: get everything connected and make the data usable.

Fast forward to today.

Now we're talking about putting AI agents into things like prior authorization and letting them make decisions across all those systems.

And honestly, I love the idea. But then you start looking at what the AI actually has to work with.
One system has one version of the patient record.

Another has something slightly different.
The clinical context might be somewhere else.

And some of the most important "rules" aren't even documented properly. They're just things someone who's been doing the job for 15 years knows.

So we're basically saying:

"Here's a really smart AI. Good luck figuring all this out."

Maybe that's why I'm becoming more convinced that the hard part of healthcare AI isn't going to be the AI itself.

It's everything underneath it.

What do you think?

Are healthcare organizations actually fixing the data foundation before deploying AI?
Or are we just getting better at putting AI on top of the mess?

When we first started talking to customers, we thought we understood the problem.

And to be fair, we understood it... at a pretty high level.

But once we started having deeper conversations with large enterprise customers, we realized we were looking at a much smaller piece of the puzzle.

The more we listened, the more we realized the actual opportunity was much bigger than we'd imagined.

It wasn't that we had to throw away what we were building.

It was that the problem was far broader and more valuable to solve than we initially thought.

Looking back, those conversations probably shaped our company more than any product brainstorm ever did.

Did this ever happen with you or with what you are building?

When we first started talking to customers, we thought we understood the problem.

And to be fair, we understood it... at a pretty high level.

But once we started having deeper conversations with large enterprise customers, we realized we were looking at a much smaller piece of the puzzle.

The more we listened, the more we realized the actual opportunity was much bigger than we'd imagined.

It wasn't that we had to throw away what we were building.

It was that the problem was far broader and more valuable to solve than we initially thought.

Looking back, those conversations probably shaped our company more than any product brainstorm ever did.

Did this ever happen with you or with what you are building?

We had one last week. Spent weeks getting ready. New features, customer requests, the whole thing.

Five minutes before the demo... the network went down.
We pushed it to the next day.
Next day, the VDI environment started acting up.

At that point we were like, "Screw it, let's just demo from a laptop."

Turns out it wasn’t really that great of an idea either. 

The laptop had different data than the customer's environment, so halfway through the demo the numbers didn't match what everyone expected.

Nothing kills your confidence faster than saying, "Just ignore those numbers..."

The funny part is the customer wasn't even upset.They'd worked with us long enough to know this wasn't the product failing, it was just one of those days where the environment wasn't on our side.

I guess that's the nice thing about building trust before you need it. 

What's the most ridiculous thing that's gone wrong during one of your customer demos?

We're getting ready for our next fundraising round, and one thing I've noticed is how different every investor conversation can be.

Some investors want to understand the market.

Some go deep into the product.

Others spend most of the meeting trying to understand the team.

We're building in healthcare AI, and the conversations I've enjoyed the most haven't really been about AI at all. They've been about the customer, why the problem has existed for so long, and why now is the right time to solve it.

It made me wonder...

For founders who have raised before, was there a moment when you felt the conversation change?

Maybe an answer you gave, a customer story you shared, or a question the investor asked that changed the tone of the meeting.

I'd love to hear what that moment was for you.

I turned 40-something recently and realized my career has been anything but linear.

I've never had a five-year plan. I've just gone all in on whatever I was curious about at the time.

I started building websites in the late '90s while I was in college. It was my first taste of selling something I had built myself.

A few years later I moved to the US and spent over a decade working across the healthcare ecosystem. Health plans, HITECH, ACA, system integrations, billing platforms... I got to see how unbelievably complex healthcare data really is.

Then I did what a lot of optimistic people do.

I started a company.

It failed.

That failure taught me more than any job ever did.

After that I went back into healthcare, built a large enterprise business, worked with public sector programs and some of the largest health plans in the US, and learned how difficult it is to change anything in a regulated industry.

Then I did something completely different.

I moved to India, bought a small dairy business despite knowing almost nothing about dairy, built a health food marketplace around it, worked with more than 1,500 farmers, and eventually exited the business.

A few years later I moved to Singapore and became obsessed with macro photography and insects.

I spent countless hours in forests and eventually discovered more than 10 species that were new to science. One was even named after me.

People ask what that has to do with technology.

Honestly... more than you'd think.

Nature taught me patience. It taught me that patterns matter. It taught me to spend more time observing before trying to solve a problem.

Around the same time AI started taking off.

Like everyone else, I was fascinated by the models.

But after spending decades in healthcare, I kept coming back to the same conclusion:

The model isn't usually the hard part.

The data is.

Today I'm building KronosX because I think healthcare doesn't have an AI problem. It has a data problem that's preventing AI from delivering on its promise.

Looking back, none of those chapters seemed connected while I was living them.

Now they all feel like they were preparing me for the same problem.

Now I want to know i am not the only one...

Has anyone else had a career that looked completely random at the time, only to realize later that all the pieces fit together?

For most of my career, I worked with some of the largest healthcare organizations in the US.

We built systems, integrated platforms, moved data around, and checked every compliance box.

Then AI arrived.

Everyone started talking about models.

But every conversation I had with health plans came back to the same questions:

• How do we safely use PHI?
• How do we make data AI-ready?
• How do we connect systems that all speak different languages?
• How do we trust what the AI is doing?

It made me realize the model wasn't the bottleneck.

The data was.

So I left a stable career and started building KronosX.

Today we're working with large US healthcare organizations, building the data layer that makes AI usable in real healthcare workflows.

It's been one of the hardest things I've ever done.

Some days it feels like we're making huge progress. Other days it feels like we're back at square one.

For founders building in healthcare:

What surprised you the most after you started talking to real customers?

For me, it was realizing the hardest technical problem wasn't AI at all.

I've been in a bunch of conversations with healthcare payors over the last few weeks, and one thing really stood out.

A year ago, everyone wanted to talk about models.

Now it feels like nobody starts there.

The questions are more like:

• How do we handle PHI?
• How do we make data usable for AI?
• How do we deal with different systems that all speak different languages?
• How do we keep the business context instead of just feeding raw data into a model?

Honestly, the model almost feels like the easy part now.

The hard part seems to be getting enterprise data into a state where AI can actually use it reliably.

Is anyone else seeing this shift, or am I just spending too much time talking to healthcare IT teams?

I've been in a bunch of conversations with healthcare payors over the last few weeks, and one thing really stood out.

A year ago, everyone wanted to talk about models.

Now it feels like nobody starts there.

The questions are more like:

• How do we handle PHI?
• How do we make data usable for AI?
• How do we deal with different systems that all speak different languages?
• How do we keep the business context instead of just feeding raw data into a model?

Honestly, the model almost feels like the easy part now.

The hard part seems to be getting enterprise data into a state where AI can actually use it reliably.

Is anyone else seeing this shift, or am I just spending too much time talking to healthcare IT teams?

Early 2025, an investor told me almost 80% of solo-founded companies fail within the first six months.

I recently crossed month 10.

This is not my first rodeo. I built and ran my last venture as a solo founder, 20+ people, four years, before I sold it.

So I know what the data is really saying. Ability is rarely the reason founders stop. Building from scratch tests you in ways nobody prepares you for.

In my last company, some days felt exciting. Other days were mentally exhausting.

What carried me through was showing up every day anyway and that experience hardened me.

How long have you been a founder for? and how whats motivating you