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How Tomato AI’s CEO Built a $100M Pipeline by Ignoring Traditional Enterprise Sales Playbooks

In a recent episode of Category Visionaries, Ofer Ronen, CEO and Co-founder of Tomato AI, shared how his team built a process orchestration platform that now manages over 100,000 processes across enterprise customers—by deliberately breaking every rule in the traditional enterprise software playbook.

The Contrarian Bet That Changed Everything

Most enterprise software companies start with a top-down sales motion. Ofer went the opposite direction. “We actually believe that in order for us to be successful, we need to have a bottoms up approach,” he explains. This wasn’t just philosophical—it was tactical. Rather than hiring expensive enterprise sales reps to chase CIOs, Tomato AI built a self-service platform that operations teams could adopt without IT approval.

The results speak for themselves. Today, Tomato AI has “over $100 million in pipeline” and works with customers running “more than 100,000 live processes” on the platform. But getting there required rejecting conventional wisdom at nearly every turn.

Why Traditional Enterprise Sales Failed Process Orchestration

The process orchestration category presents a unique GTM challenge. Unlike point solutions that solve one specific problem, Tomato AI enables teams to orchestrate workflows across any system. As Ofer describes it: “Orchestration is one of these things that can be applied in so many different ways across your organization.”

This versatility is both Tomato AI’s greatest strength and its biggest GTM obstacle. When Ofer’s team initially tried traditional enterprise sales, they hit a wall. Sales reps couldn’t effectively communicate the platform’s value because “the use cases are so diverse.” A workflow that procurement uses to manage vendor onboarding looks nothing like the process legal uses for contract approvals—yet both run on Tomato AI.

The breakthrough came from inverting the model entirely. Instead of sales reps explaining what Tomato AI could do, they let users discover it themselves.

Building the Self-Service Engine

Ofer’s team made a critical architectural decision early: “We built the product to be completely self-service.” This meant users could “connect it to all the different systems that they have, build a process, launch it into production” without ever talking to a Tomato AI employee.

But self-service at the enterprise level isn’t just about product design—it’s about trust architecture. Tomato AI invested heavily in security and compliance early, achieving SOC 2 Type 2 certification and building robust data governance features. “We actually have more enterprise-grade security than Workato, than Zapier, than any of these other guys,” Ofer notes.

This investment paid off in unexpected ways. While competitors focused on SMB self-service, Tomato AI’s enterprise-ready infrastructure allowed them to land Fortune 500 accounts through a bottoms-up motion. Operations teams could start using Tomato AI within their existing security parameters, then expand usage organically.

The AI Timing Advantage

Tomato AI’s recent acceleration hasn’t happened in a vacuum. The company raised their Series B “right around when ChatGPT launched,” giving them capital exactly when AI transformed their market positioning. As Ofer explains: “AI actually makes everything that we’re doing much, much easier and much better.”

The AI wave solved a critical adoption challenge. Previously, building workflows required technical expertise. Now, “someone can just describe what they want to do” and Tomato AI’s AI agents can build and modify processes. This dramatically lowered the barrier to entry while increasing the platform’s power.

But Ofer doesn’t view AI as just a feature—it’s a fundamental shift in how work gets done. “The way that people work is going to completely change,” he predicts. Rather than employees manually executing tasks, “AI agents are going to do most of that work.” Tomato AI positions itself as the orchestration layer that manages these AI agents, ensuring they work within proper governance and compliance frameworks.

From 30 to 100+ Employees: Scaling the Model

Growth created new challenges. When Tomato AI had 30 employees, the bottoms-up model worked beautifully. At over 100 employees, Ofer’s team had to add structure without losing their self-service DNA.

The solution was a hybrid approach. Tomato AI still maintains its self-service product, but now layers on what Ofer calls “high-touch support.” This means customers can still start and expand on their own, but they have access to Tomato AI experts when they need them. “We’re definitely more high touch than we used to be,” Ofer acknowledges, “but we still want to be much less high touch than these traditional vendors.”

This balance is intentional. Traditional enterprise software companies assign dedicated implementation teams to each customer. Tomato AI provides resources and guidance but expects customers to own their processes. As Ofer puts it: “These processes belong to the business teams. They’re not IT-owned processes.”

The Category Creation Challenge

Perhaps Tomato AI’s biggest ongoing challenge is category definition itself. Process orchestration sits at the intersection of workflow automation, integration platforms, and business process management—but it’s distinct from all three.

“We’re actually creating a new category,” Ofer explains. This means every conversation starts with education. Prospects need to understand not just what Tomato AI does, but why process orchestration matters as a separate category.

The bottoms-up motion helps here too. Rather than convincing executives that they need a new category of software, Tomato AI lets practitioners experience the value firsthand. When someone builds their first orchestrated process and sees it working across multiple systems, the category makes sense immediately.

The $100M Pipeline Formula

Tomato AI’s path to a nine-figure pipeline came from compounding several contrarian decisions: building enterprise-grade infrastructure for a self-service model, investing in AI capabilities before the ChatGPT moment made them obvious, and maintaining a bottoms-up motion even as they scaled into large enterprises.

“We’re definitely going after larger organizations,” Ofer confirms, but the approach remains unchanged: let the product demonstrate value, let users discover applications, and let adoption spread organically. This model might take longer than traditional enterprise sales, but it creates stickier customers and more sustainable growth.

For B2B founders building in complex, horizontal categories, Ofer’s journey offers a clear lesson: sometimes the fastest path to enterprise adoption isn’t through the C-suite door—it’s through empowering the teams doing the actual work.