Pagos and the $44M Bet on a Problem Even PayPal Couldn’t Solve

Pagos and the $44M Bet on a Problem Even PayPal Couldn’t Solve

When PayPal—a company built on payments technology—can’t efficiently compare its own vendor performance against competitors, something is fundamentally broken.

Klas Bäck lived this dysfunction. His team at PayPal had optimized their infrastructure. They had data proving superior performance. A basic business conversation that should have taken weeks consumed months.

“We could not have that conversation with a single one of our largest customers without months of comparing data,” Klas recalls. “What are you looking at? That data doesn’t look real. Are you sure that’s your right data?”

In a recent episode of Category Visionaries, Klas Bäck, CEO and Co-Founder of Pagos, a payments operations platform that’s raised $44 million in funding, explained how he recognized this wasn’t just a PayPal problem—it was an industry-wide opportunity.

The Signal Hidden in Organizational Pain

Most founders worry their problem isn’t “big enough.” Klas had a different signal: if PayPal couldn’t solve this problem despite unlimited resources and top talent, the problem was structural.

This reveals a crucial principle. The best startup opportunities often emerge where large, sophisticated companies fail to fix internal dysfunction. Not because they’re incompetent, but because solving it doesn’t align with their core business.

PayPal’s business is processing payments, not building data infrastructure. The data comparison problem was painful but not critical enough to justify a major internal initiative. This gap—too painful to ignore, too peripheral to fix—creates the opening for startups.

When Industry Opacity Signals Opportunity

The payments data problem existed within broader industry dysfunction. “The payment industry operates in quite opaque world,” Klas observes. “Everything is complicated and people say a lot of things that they don’t sell and back up with back.”

This opacity wasn’t accidental. When comparing vendors requires months of reconciliation, switching costs stay high. When metrics lack standard definitions, proving superiority becomes impossible.

Klas recognized that opacity plus high cost creates opportunity. “There’s not a single company out there that are selling or billing online that are not finding it challenging to optimize their own payments infrastructure,” he explains. “That means that they are leaving money at the table.”

The validation came from scale. This wasn’t niche—it was universal among companies processing payments online.

The Data-Driven Blind Spot

What made the problem striking was the contrast. “Even extremely large companies that are maybe selling billions of worth of products on any given year, they will not accept not being data driven for anything else. Except when it comes to payments, there is often the data is incomplete, that is too old, that is not correct.”

Sophisticated companies obsessively track every metric. But for one of their largest cost centers—payments operations—they operate blind.

This contradiction validated the opportunity. When smart people do something suboptimal, it’s usually not because they’re dumb. It’s because the current solution space makes the optimal choice impossibly difficult.

The Conversations That Confirmed the Pattern

Validation came from pattern recognition across customer conversations. Enterprise companies processing significant volume all struggled with the same issues: incomplete data, months-long vendor comparisons, inability to optimize.

The sophistication of buyers accelerated validation. “Some are extremely well funded or have a lot of resources. So they have maybe built something in house,” Klas notes. “That’s actually been the easiest group because they know what they want and we can show pretty quickly that we can do it better.”

When potential customers have already built their own solution, you’ve validated both the problem and its severity. They’ve passed the ultimate commitment test: spending engineering resources on it.

The Three-Tier Market Structure

Pagos discovered three distinct customer segments revealing different aspects of the opportunity.

The first had built in-house solutions, validating the problem’s importance. The second knew they had optimization opportunities but hadn’t prioritized solving them. They needed education about what was possible.

The third, and largest, didn’t fully grasp the complexity. “Payments, is a weird combination of a little bit of finance, lots of technology and very complicated domain knowledge,” Klas explains. “Like, surely it can’t be that complicated. It’s like, no, actually it’s very complicated and it’s hard to do well.”

This structure validated market size. The immediate opportunity (tier one) proved the problem. The medium-term opportunity (tier two) showed expansion potential. The long-term opportunity (tier three) revealed category-creation upside.

The Organizational Fragmentation Signal

Another validation signal emerged from organizational structure. When Klas asked who owned payments operations, answers varied wildly.

“It could be in finance for traditional reasons, or it could actually be owned by product,” Klas explains. “It could be sitting on an engineering sort of.”

This organizational ambiguity validated the problem. When no one clearly owns something important, it falls through the cracks. Payments touched finance (cost), product (experience), and engineering (infrastructure). The cross-functional nature made it hard to own.

From Problem Validation to Category Creation

The opportunity was creating a category. “We built the payments data company where we help people with analytics, visualization, baseline what their metrics are, track them over time and then give them tools to help them better execute,” Klas explains.

Rather than positioning as one solution among many, Pagos defined themselves as the payments data company. The category creation was validated by the fact that no clear leader existed despite the universal problem.

Looking forward, Klas sees the validated problem expanding. “Our core focus is on companies that are selling and billing online and we are fortunate there are many of those. There’s probably in the core segment, maybe 100,000 of it in the US.”

The Framework for Recognizing Similar Opportunities

The Pagos story reveals a framework for validating problems in opaque industries:

Look for problems that sophisticated companies can’t solve internally despite resources. Identify industries where opacity serves incumbent interests but frustrates buyers. Find contradictions where smart people do obviously suboptimal things. Listen for customers who’ve already built or attempted to build solutions. Map organizational fragmentation that prevents problems from being owned. Validate that the problem is universal, not niche.

When these signals align, you’ve found more than a problem worth solving. You’ve found a category worth creating.

The $44 million bet on payments data wasn’t a leap of faith. It was a calculated recognition that when even PayPal can’t solve a problem efficiently, the dysfunction is deep enough to build on.