Find the Pain or Build Forever with Sridhar Uyyala

On this episode of the Predictable Revenue Podcast, Collin Stewart sits down with Sridhar Uyyala from TensorLinks to walk through what finding product-market fit actually looks like in the wild: messy, slow, and full of false starts.

This post breaks down that journey, not to retell it, but to show where founders actually go wrong, and why most “good ideas” fail long before the market is ready for them.

Most founders start with a vision.

Sri set out to build AI assistants for self-driving cars, something technically impressive, forward-looking, and, at the time, completely disconnected from real demand.

The idea made sense on paper. If cars are going to be autonomous, they’ll need intelligent interfaces. So he started building toward that future.

The problem? That future didn’t exist yet.

The underlying tech wasn’t ready, voice was rigid and unreliable, and conversations felt robotic. LLMs didn’t exist in any usable form. Even the hardware layer made distribution unrealistic.

And the market wasn’t there either. No customers actively seeking a solution like this.

• No urgency.
• No budget.
• No pull.

So what do you have at that point? Just a prototype and a guess.

This is where a lot of founders get stuck. 

The product feels right because it’s ambitious and technically interesting. But “interesting” doesn’t convert into usage or revenue.

From the outside, it looked like failure, and the obvious conclusion was that the idea didn’t work.

But the real problem was timing. Being early and being wrong produce the same signals.

• No demand. 
• No revenue. 
• No pull.

If you don’t recognize that distinction, you either quit too soon or, worse, keep pushing a market that isn’t ready.

The Wandering Phase

After that, Sri did what most technical founders do when something doesn’t work: he kept building.

He moved from one idea to the next: an email assistant, a financial analysis tool, a document Q&A product. Each one made sense in isolation, and each one was built on the same underlying belief that somewhere in this space, there had to be a real opportunity.

But none of them got traction.

There were no consistent users, no clear demand, and no strong signal that any of these products were solving a problem painful enough. It looked like iteration from the outside, but in reality, it was just a series of disconnected bets.

Building starts to feel like progress, but without direct, continuous feedback from real users, it’s just guesswork. And guesswork, even when executed well, doesn’t compound.

Building isn’t learning.

The Inflection Point

The shift came from a better question.

Instead of building another product, Sri started talking to people. Through a referral, he was introduced to an operator who runs multiple dental clinics. And for the first time, he didn’t lead with a pitch. 

He asked a simple question: What are your biggest problems? The answer was immediate and specific. They were missing calls, a lot of them.

And missed calls were lost revenue. New patients couldn’t book, and existing patients couldn’t reschedule. Across multiple clinics, this was a compounding operational leak.

This time, the problem was clear, measurable, and already costing the customer money. It didn’t need to be explained or sold. That’s the inflection point most founders miss.

Product-market fit starts with a problem so painful that someone is actively looking for a solution.

First Real Signal of PMF

He built a solution around that one problem, handling and analyzing calls, and the clinic paid for it. That alone was new. 

After multiple products with no traction, someone was now willing to invest in solving the problem he was addressing.

But the stronger signal was what happened next.

The same problem showed up across multiple locations. The solution wasn’t tied to a single edge case or one-off need. It applied across the entire group of clinics, and the value held up each time. That’s when it starts to shift from “this works” to “this might scale.”

Revenue plus repeatability, that’s the first real signal.

The Second Mistake

With a paying customer and a clear problem, it looked like he had momentum. So he did the natural thing: he tried to expand.

Instead of focusing just on calls, he started building toward a broader vision: an AI system that could handle all clinic operations. Scheduling, workflows, internal processes, everything.

On paper, it made sense. If one part of operations could be automated, why not the rest? But this is where things started to break.

The broader the scope, the harder it becomes to define the product, communicate its value, and replicate the solution for other customers. What worked for one clinic didn’t cleanly translate to another, and the problem space became messy again.

That’s the hidden cost of going too broad too early. The more you try to solve, the harder it is to scale anything.

The Breakthrough

Instead of trying to solve the entire operational stack, he narrowed the focus to one specific function: the front desk. Calls, scheduling, patient communication, everything that happens before a patient walks in.

That shift changed everything. Now the product was easy to understand, easy to explain, and easy to map to a real, existing role inside the clinic. Instead of a vague “AI for operations,” it became something concrete: an AI front desk.

And with that clarity came scalability.

The problem was consistent across clinics, the use case didn’t change, and the value was obvious: fewer missed calls, more booked appointments, better utilization. This is what narrowing actually does.

What happened next was subtle, but important.

With less than $1,000 in ad spend, he generated around 60 demos and closed roughly 10 customers. The mechanics of distribution didn’t change much. What changed was how the market responded.

By then, buyers were already looking, they understood what AI could do in their workflows, the problem was familiar, and the solution was easy to map.

This is what product-market fit actually feels like in practice.

It doesn’t show up as a single turning point but as a gradual shift: early customers become repeatable wins, the same problem keeps appearing across accounts, and demand becomes easier to capture because it already exists in the market.

Product-market fit isn’t binary. It builds over time, and as it strengthens, everything downstream works with less effort.

Conclusion

Most founders start too early, build too broadly, and mistake activity for progress. Then they try to fix it with more features or more marketing, when the real issue is that the problem isn’t sharp enough, or the market isn’t ready.

Sri won because he got closer to a real problem, narrowed his scope, and stayed long enough for the market to catch up.

That’s the job: Find an existing pain point, focus until it’s repeatable, and wait for the market to start pulling. Everything else is just noise.

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