The Story of Tomato AI: Building the Operating System for Enterprise Work in the Age of AI

The best enterprise software companies don’t start with a product vision. They start with a problem that won’t go away.

In a recent episode of Category Visionaries, Ofer Ronen, CEO and Co-founder of Tomato AI, shared how his journey from Stanford researcher to enterprise software founder led to building what might become the operating system for how work gets done in the age of AI.

The Research Origins

Tomato AI’s story begins in academia, where Ofer was studying a problem that seemed purely theoretical at the time: how do you coordinate complex processes across multiple systems without central control?

“I was doing research at Stanford,” Ofer recalls, working on distributed systems and process coordination. The academic questions were abstract, but the underlying challenge was universal. Every organization runs on processes—procurement, legal approvals, customer onboarding—and these processes span dozens of disconnected systems.

The traditional answer was integration platforms or workflow tools. But Ofer saw something different. These weren’t integration problems or workflow problems. They were orchestration problems. The systems could talk to each other fine. What was missing was an intelligent layer that could coordinate actions across all of them based on business logic.

This distinction would become Tomato AI’s foundation.

The First Customer Discovery

Like many technical founders, Ofer initially built for the wrong buyer. The early Tomato AI product was sophisticated and powerful, but it assumed IT would be the champion. They were wrong.

“These processes belong to the business teams. They’re not IT-owned processes,” Ofer explains. This realization completely reshaped the company. IT owned the systems, but business teams owned the processes running across those systems.

This insight led to a fundamental pivot. Rather than building an IT tool that required technical expertise, Tomato AI would build a platform that business users could operate themselves. Procurement teams could build their own vendor onboarding workflows. Legal teams could create their own contract approval processes. Finance could orchestrate their own quote-to-cash operations.

The architectural implications were massive. “We built the product to be completely self-service,” Ofer notes. Every decision—from the interface design to the security model to the deployment architecture—had to work for non-technical business users operating independently.

The Security Paradox

Most self-service products target SMBs. Tomato AI made a contrarian bet: build self-service for the enterprise.

This created an unusual challenge. Enterprise customers demanded bank-level security and compliance. Self-service platforms typically had minimal security features. Tomato AI needed both.

“We actually have more enterprise-grade security than Workato, than Zapier, than any of these other guys,” Ofer states. This wasn’t marketing hyperbole. Tomato AI invested heavily in SOC 2 Type 2 certification, comprehensive data governance, and enterprise security features—all while maintaining a self-service experience.

This combination proved to be their secret weapon. While competitors chose between self-service simplicity or enterprise security, Tomato AI offered both. A Fortune 500 operations manager could start using Tomato AI in the morning without IT approval, yet the platform met every security requirement their CISO demanded.

The Use Case Explosion

As customers started using Tomato AI, something unexpected happened. Each team found completely different applications.

“Orchestration is one of these things that can be applied in so many different ways across your organization,” Ofer explains. Procurement used it for vendor management. Legal used it for contract workflows. Finance used it for invoicing. IT used it for ticket routing. Sales ops used it for deal approvals.

For most companies, this diversity would be a positioning nightmare. How do you market a product that does everything? Ofer realized they shouldn’t try. Instead of forcing a single use case narrative, they built a platform truly flexible enough to support them all.

This created organic virality within enterprises. Someone in procurement builds a workflow and shares it with a colleague. That colleague adapts it for their own needs. Soon, multiple teams across the organization are orchestrating processes on Tomato AI—each solving their own unique problems.

The result: “more than 100,000 live processes” running on the platform today.

The AI Inflection Point

Tomato AI raised their Series B “right around when ChatGPT launched.” The timing transformed everything.

“AI actually makes everything that we’re doing much, much easier and much better,” Ofer explains. Previously, building a process required understanding workflow logic and system integrations. With AI, “someone can just describe what they want to do” and Tomato AI builds it.

But Ofer sees something bigger happening. AI isn’t just making Tomato AI easier to use—it’s validating the entire category thesis.

“The way that people work is going to completely change,” he predicts. Humans won’t manually execute tasks anymore. AI agents will do that work. But those agents need coordination. They need governance. They need orchestration.

This is Tomato AI’s future. Not just orchestrating human workflows across systems, but orchestrating AI agents across the enterprise. Making sure the procurement agent and the legal agent and the finance agent work together coherently, within proper compliance boundaries.

Scaling While Staying True

Growing from 30 employees to over 100 brought inevitable pressure to adopt traditional enterprise software practices. Long implementation cycles. Dedicated customer success teams. Top-down sales.

Ofer resisted. “We’re definitely more high touch than we used to be,” he acknowledges, “but we still want to be much less high touch than these traditional vendors.”

The distinction matters. Traditional enterprise software does the work for customers. Tomato AI guides customers doing their own work. This preserves the self-service DNA while providing the support enterprises need.

It also creates better outcomes. When customers build their own processes, they understand them deeply. They can modify them as needs change. They’re not dependent on vendor professional services.

Building the Future of Work Infrastructure

Today, Tomato AI manages “over $100 million in pipeline” and orchestrates processes for Fortune 500 companies. But Ofer’s vision extends far beyond current scale.

He sees Tomato AI becoming infrastructure—the layer that every enterprise runs on top of. Just as operating systems manage applications on computers, Tomato AI will manage processes and AI agents across enterprises.

“We’re actually creating a new category,” Ofer explains. Process orchestration isn’t workflow automation. It isn’t an integration platform. It isn’t business process management. It’s the missing layer that makes all of those things work together intelligently.

As AI agents proliferate across enterprises—agents for sales, for support, for operations, for finance—they’ll need coordination. They’ll need governance. They’ll need someone ensuring they work together toward business objectives rather than at cross purposes.

That’s Tomato AI’s future: the operating system for enterprise work in the age of AI. Not replacing humans or replacing systems, but orchestrating everything—human and machine—into coherent, governed, business-aligned processes.

From Stanford research to 100,000 processes to the infrastructure layer for AI-powered work, Tomato AI’s story is still early. The operating system for the future of work is just beginning to be written.