The Story of PolyAPI: Building the Missing Layer Between AI and Enterprise Software

The Story of PolyAPI: Building the Missing Layer Between AI and Enterprise Software

The tool that would eventually become PolyAPI wasn’t supposed to be a product at all. It was supposed to solve one annoying problem on one consulting project, and then everyone would move on. But sometimes the most important companies start as workarounds that refuse to stay quiet.

When the Workaround Becomes the Product

Darko Vukovic and his co-founder were deep in a consulting engagement when they hit the wall that every enterprise developer knows intimately: the systems wouldn’t talk to each other. “We were doing a consulting project and we had to integrate with a bunch of systems that our customer was using,” Darko recalls in a recent episode of Category Visionaries. “And it was really difficult, really expensive, really time consuming.”

Most consulting teams would have suffered through it, billed the hours, and moved to the next project. Darko’s team did something different. “We ended up building this internal tool to make that work smoother, work better, work faster.” It was meant to be internal scaffolding—the kind of thing you build, use once, and forget about.

Except their clients wouldn’t let them forget about it. Other consulting customers started asking questions. They had the same integration nightmares. They wanted access to that internal tool. The requests kept coming, and Darko realized they were seeing something bigger than a one-off solution.

“We kind of said, you know what, let’s take a step back. This problem of integrating lots of systems together, this is a category in and of itself. Why don’t we just focus on this?” It was the kind of pivot that sounds obvious in retrospect but requires real conviction in the moment. They were walking away from predictable consulting revenue to build a product in a space that didn’t have a clear category definition.

The Invisible Tax Nobody Talks About

The challenge wasn’t just building the product—it was explaining why anyone should care. Integration problems are invisible until they’re catastrophic. Engineering teams know the pain intimately, but executives often don’t realize how much it’s costing them.

Darko found the frame that made the problem impossible to ignore. “Companies spend 30 to 40% of their engineering time doing integrations,” he explains. “That means if you have 100 engineers, you have 30 to 40 engineers that are just doing integration work.”

Think about that for a moment. A company with a hundred engineers has thirty to forty people whose primary job is connecting systems—not building features, not shipping products, just making software talk to other software. It’s a massive hidden tax that shows up nowhere on the P&L but everywhere in opportunity cost.

This reframing became central to PolyAPI’s go-to-market strategy. They weren’t selling integration tools—they were selling engineering capacity back to companies that didn’t realize they’d lost it.

Architecture as Strategy

The technical decisions Darko made early on would define what kinds of customers PolyAPI could serve. In a world moving toward cloud-based SaaS, PolyAPI made a counterintuitive choice: they built for on-premise deployment.

“We actually sit in your infrastructure,” Darko explains. “So PolyAPI, you can run it on-prem, you can run it in your own AWS account, you can run it in your own Google Cloud account, Azure account, whatever you want.”

This wasn’t about technical preference—it was about market access. Banks, healthcare companies, government contractors—the organizations with the biggest integration problems and the largest budgets—have strict data residency requirements. They can’t use cloud-based integration platforms no matter how good they are. By choosing an architecture that could deploy anywhere, PolyAPI opened doors that would stay closed to their competitors.

The architectural choice also drove product positioning. “We are not an iPaaS. We are an API management platform,” Darko insists. The distinction matters because iPaaS platforms require data to flow through their infrastructure. PolyAPI’s approach is fundamentally different, and that difference is the entire value proposition for their target customers.

The Category That Didn’t Exist

Selling infrastructure is hard. Selling infrastructure for a category that doesn’t exist yet is harder. Darko’s approach was decidedly unglamorous: “We did a lot of cold outreach, a lot of cold emails, a lot of cold calls.”

But the targeting was surgical. They focused on companies with 200-2000 employees who were already spending heavily on integration tools. The logic was simple: “If you are spending this much money on integration tools, you probably have a very deep integration problem.” They weren’t prospecting to companies that might need their solution someday. They targeted organizations already bleeding budget on inadequate alternatives.

The message had to do two things simultaneously: educate prospects about why their current approach wasn’t working, and explain why PolyAPI’s architecture solved problems they didn’t know they could solve. “When you are creating a new category and people don’t really know about your category, you have to do a lot of education,” Darko notes. That education became a core part of their content strategy.

“You need to be seen as a thought leader in your space,” Darko argues. “You need to be putting out useful content that is going to resonate with your ICP.” For PolyAPI, that meant producing content about API management, integration challenges, and infrastructure decisions—topics that establish expertise rather than push product.

The AI Wave Nobody Expected

Then AI happened. “AI kind of came out of left field and took over the world,” Darko admits. Suddenly, every company wanted AI agents and copilots. But they quickly discovered a fundamental problem: AI that can’t take actions is just an expensive chatbot.

“If your company uses Salesforce, HubSpot, NetSuite, whatever, your AI needs to talk to all these systems to be able to do work on your behalf,” Darko explains. The infrastructure PolyAPI had been building for four and a half years—the API management layer that connected enterprise systems—became critical for AI deployment.

“We very quickly realized, oh, this thing that we’ve been building for four and a half years is actually very useful for AI.” The product didn’t need to change. But everything else did. “We realized that the best way to think about PolyAPI is to serve as the connection layer between your AI and the rest of your business.”

Their homepage, pitch decks, sales conversations—everything got reframed around AI enablement. It was a messaging pivot that required zero product changes but completely transformed how prospects understood PolyAPI’s value. Companies that were lukewarm about solving integration problems suddenly became urgent about enabling their AI investments.

Building Toward the Future

Where does PolyAPI go from here? Darko sees a future where AI agents are as common in enterprises as software applications are today. “Every company is going to have lots of AI agents,” he predicts. “Those AI agents are going to need to interact with your business systems.”

The vision isn’t just about fixing integration problems—it’s about becoming fundamental infrastructure for how enterprises operate in an AI-native world. PolyAPI wants to be the layer that makes enterprise AI actually useful, the connection tissue that turns chatbots into agents that can take real action.

It’s an ambitious goal that extends far beyond the consulting project that started it all. But then again, most important infrastructure starts as a workaround to an annoying problem. The difference is recognizing when that workaround is actually solving something fundamental—and having the conviction to build it into something that can scale. That’s the story of PolyAPI: a tool that refused to stay internal, solving a problem that turned out to be bigger than anyone realized.