How 1,000+ Customer Calls Shaped A Breakout Enterprise AI Startup

Enterprise AI Startup Narada: 1,000 Customer Calls Changed Everything

How do you build an enterprise AI company that actually works? For David Park, the answer started with picking up the phone — over and over again. Narada, his enterprise AI startup, uses large action models to automate complex, multistep workflows across enterprise systems. And it now counts major enterprise names among its clients. But the path there looked nothing like a typical Silicon Valley success story.

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The Enterprise AI Problem Nobody Talks About

Enterprise software is littered with AI tools that promise transformation but deliver frustration. Most solutions bolt on automation as an afterthought, leaving teams juggling disconnected systems and manual handoffs. Park, a veteran founder and former Startup Battlefield competitor, recognized this gap early. He and his team — a group of experienced researchers and operators from two of the world's leading universities — set out to solve automation at the workflow level, not just the task level. That distinction, it turns out, makes all the difference.

Narada's core technology centers on large action models, a category of AI designed to handle multi-step processes that span across different enterprise platforms. Think of it as AI that doesn't just answer a question — it takes a sequence of actions to complete an entire job. That's a meaningfully harder engineering problem, and solving it well is exactly what's been turning heads in the enterprise world.

Why 1,000+ Customer Calls Became the Real Product Roadmap

Before writing a line of production code, Park and his team did something that's become increasingly rare in the startup world: they listened. More than 1,000 customer calls shaped Narada's product roadmap from the ground up. These weren't casual check-ins — they were deep discovery sessions designed to understand exactly where enterprise workflows break down, where time is wasted, and where automation could deliver real, measurable value.

This approach reflects a principle that top founders swear by but few actually execute with this level of discipline. Park calls it intentional iteration. Rather than rushing to build features based on internal assumptions, the Narada team used customer conversations as their most reliable data source. Every call added a layer of nuance to the product. By the time they were ready to scale, the solution was already calibrated to real-world enterprise pain — not hypothetical use cases.

The result? A product that enterprise buyers didn't need to be convinced of. It already spoke their language because it was built from their feedback.

The Fundraising Strategy That Surprised Everyone

When Narada entered Startup Battlefield in 2024, the team turned heads — but not entirely for the reasons you might expect. Despite having a world-class founding team, enterprise customers, and a product that demonstrably worked, the startup had raised remarkably little capital at that point. For most observers in the venture world, that seemed almost counterintuitive. Investors typically chase exactly this kind of profile.

But the lean fundraising approach was entirely intentional. Park's reasoning is sharp and counterintuitive: too much money too early is a liability, not an asset. "When you have too much money in the bank and you are not near product-market fit, you're tempted to just spend money on things that actually don't help you evolve the company in the right way," Park explained. "It removes the friction to do a lot of wrong things."

That's a philosophy that runs counter to the conventional startup playbook, which often prizes large funding rounds as markers of success. Park reframes funding as a tool — one that should be deployed only when a company knows precisely what it's buying. Without product-market fit as a foundation, a large war chest can mask fundamental problems rather than solve them.

What Large Action Models Actually Do in the Enterprise

It's worth slowing down on the technology itself, because "enterprise AI" has become a term that's been stretched to cover everything from chatbots to predictive analytics. Narada's approach is more specific and, frankly, more ambitious. Large action models go beyond generating text or surfacing insights — they execute. They can navigate enterprise systems, trigger actions across platforms, and complete workflows that previously required human coordination at every step.

For enterprise teams, this means a category of automation that was simply not possible before. Imagine a multi-step procurement process that spans an ERP system, an email thread, and a vendor portal — all completed end-to-end by an AI model that understands context, handles exceptions, and escalates appropriately when human judgment is needed. That's the level of sophistication Narada is operating at. And that's why enterprise customers aren't just piloting the product — they're integrating it into core operations.

The technical credibility behind this is real. The founding team's background in research and enterprise operations gave Narada the rare combination of academic rigor and practical understanding needed to build AI that works in messy, real-world enterprise environments.

Scaling With Intention: How Narada Is Growing Differently

Growth at Narada doesn't look like hypergrowth for its own sake. Park has been deliberate about scaling in a way that preserves the product quality and customer intimacy that built the company's reputation. Each new enterprise customer gets the same level of attention that the first ones did. That consistency is hard to maintain as headcount and customer counts grow — but it's also the reason Narada's retention and expansion metrics are strong.

This intentional approach to scaling is increasingly rare, and increasingly valuable. In an era when enterprise AI vendors are racing to announce the largest customer logos and the biggest funding rounds, Narada is quietly compounding. The team focuses on depth over breadth, ensuring each customer deployment is a success story before pursuing the next one. That methodical pace is building something that large funding rounds and aggressive sales motions often destroy: genuine trust.

What Other Founders Can Learn From Narada's Playbook

Park's journey with Narada offers a clear-eyed alternative to the "raise big, move fast, figure it out later" model that still dominates startup culture. His playbook is built on three principles that are deceptively simple but genuinely hard to execute.

First, talk to customers obsessively before building. A thousand calls isn't an exaggeration — it's a commitment to understanding the problem before falling in love with a solution. Second, treat capital as a constraint that creates discipline, not a resource that creates freedom. Money spent before product-market fit is often money that slows you down. Third, scale only what's working, and only when you're confident it's working. Premature scaling is one of the most common reasons strong products fail to become strong companies.

Enterprise AI is one of the most competitive categories in technology right now. What separates the startups that break through from the ones that stall isn't just the quality of the technology — it's the judgment of the founders building it. In that regard, Narada's story is less about artificial intelligence and more about the very human discipline of doing hard things carefully.

Narada continues to grow its enterprise customer base in 2026, with David Park sharing lessons from the company's journey on the Build Mode podcast.