Tomato AI’s Playbook: Building Enterprise-Grade Security for Self-Service Adoption

Tomato AI’s Playbook: Building Enterprise-Grade Security for Self-Service Adoption

Self-service platforms target SMBs. Enterprise platforms require security reviews, implementation teams, and six-month sales cycles. This binary has governed B2B software for decades.

In a recent episode of Category Visionaries, Ofer Ronen, CEO and Co-founder of Tomato AI, explained how his team shattered this assumption. By investing in enterprise-grade security while maintaining complete self-service functionality, Tomato AI unlocked a GTM motion that competitors thought impossible: Fortune 500 companies adopting software without talking to sales.

The result is a platform now managing over 100,000 processes with “over $100 million in pipeline.” But getting there required making expensive technical bets that most self-service companies avoid.

The Impossible Positioning Problem

Tomato AI faced a dilemma that would kill most startups. Their product needed to be simple enough that non-technical business users could operate it independently. But it also needed to meet the security requirements of Fortune 500 companies connecting to their most sensitive systems.

“We built the product to be completely self-service,” Ofer explains. Users needed to “connect it to all the different systems that they have, build a process, launch it into production” without IT involvement. This meant intuitive interfaces, zero-code workflow builders, and instant deployment.

But those same users worked at companies with strict security policies. Their CISOs demanded comprehensive audit logs, granular access controls, and compliance certifications. Their legal teams required data residency options and detailed security documentation. Their procurement teams needed vendor security assessments.

Most companies solve this tension by choosing a target customer. Build for SMBs and accept that enterprise sales will be hard. Or build for enterprise and accept that self-service adoption won’t happen.

Ofer refused to choose. Instead, Tomato AI would build infrastructure that satisfied both requirements simultaneously.

Outbuilding the Competition on Security

The technical investment required wasn’t trivial. While competitors focused on making their products easier to use or adding more integrations, Tomato AI allocated significant engineering resources to security infrastructure.

“We actually have more enterprise-grade security than Workato, than Zapier, than any of these other guys,” Ofer states. This wasn’t marketing positioning—it was architectural reality.

Tomato AI pursued SOC 2 Type 2 certification early, before most self-service platforms consider it necessary. They built comprehensive data governance features that let enterprises control exactly how data flowed through workflows. They created audit trails detailed enough to satisfy financial services compliance teams. They implemented role-based access controls granular enough for global organizations with complex permission structures.

Each of these investments cost engineering time that could have gone toward features users requested. Each delayed product improvements that competitors were shipping. But each also created competitive moat.

When a Fortune 500 operations manager wants to start using a workflow automation tool, their first obstacle is usually the security review. Most self-service platforms fail here. They lack the certifications, documentation, and features that enterprise security teams require. The operations manager has to either abandon the tool or trigger a lengthy implementation process involving IT.

Tomato AI eliminated this friction entirely. Operations managers could start using the platform immediately because it already met their company’s security requirements.

The Self-Service Security Architecture

The technical challenge wasn’t just achieving enterprise security—it was achieving it without breaking the self-service experience.

Traditional enterprise software handles security through controlled implementation. Vendors deploy the software within the customer’s infrastructure, configure security settings during implementation, and train admins to maintain those settings. Security happens through restriction and oversight.

Tomato AI needed a different architecture. Business users had to be able to connect systems, build workflows, and deploy processes independently—all while maintaining security controls that satisfied CISOs.

This required building security intelligence into the platform itself. Rather than relying on implementation teams to configure security correctly, Tomato AI automated security enforcement. Data governance policies applied automatically. Access controls adapted to organizational structures without manual configuration. Compliance requirements were built into the workflow engine rather than added as afterthoughts.

The result: users experienced complete freedom to build and deploy processes, while CISOs saw comprehensive security controls operating behind the scenes.

Where Business Teams and Security Teams Align

The architecture solved another critical problem: ownership boundaries between business teams and IT.

“These processes belong to the business teams. They’re not IT-owned processes,” Ofer explains. But business team ownership doesn’t mean ignoring security. It means building security infrastructure that works without requiring IT to own the processes themselves.

Tomato AI’s model lets business teams own their workflows while IT owns the security framework. A procurement manager can build and modify vendor onboarding workflows without IT approval. But those workflows operate within security guardrails that IT configured once at the organizational level.

This separation of concerns unlocked viral adoption within enterprises. Business teams could move fast and solve their own problems. Security teams could maintain control without becoming bottlenecks. Everyone got what they needed.

The GTM Unlock

The technical investment in enterprise security while maintaining self-service created a unique competitive position. Tomato AI could compete against enterprise platforms without requiring enterprise sales cycles, and compete against self-service platforms without sacrificing enterprise requirements.

When prospects evaluate workflow automation tools, they typically face this choice: easy self-service tools that fail security review, or enterprise-grade platforms that require six-month implementations. Tomato AI offered a third option: enterprise security with zero implementation time.

This positioning proved especially powerful with sophisticated buyers. Operations managers at Fortune 500 companies are frustrated by lengthy implementation cycles, but they’re not willing to use tools that violate security policies. Tomato AI gave them both speed and compliance.

The viral adoption pattern this enabled became Tomato AI’s core GTM engine. An operations manager starts using Tomato AI, passes it through their security team, then shares it with colleagues. Those colleagues build their own workflows and share with their teams. Adoption spreads organically because there’s no friction—both the user experience and the security review process are frictionless.

The Ongoing Investment

As Tomato AI scaled from 30 to over 100 employees, the security investment didn’t stop. While some companies treat security as a one-time checklist, Ofer’s team treats it as ongoing infrastructure.

“We’re definitely more high touch than we used to be,” Ofer acknowledges, referring to customer support. But being high-touch doesn’t mean reverting to traditional implementation models. “We still want to be much less high touch than these traditional vendors.”

The distinction matters for security too. Traditional vendors handle security through dedicated implementation and support. Tomato AI handles it through platform architecture. As customer needs evolve, Tomato AI builds new security capabilities into the product rather than adding service teams.

This scales better. A service-based security model requires hiring linearly with customer count. An architecture-based security model scales with engineering investment, not headcount.

The Broader Lesson

Tomato AI’s path reveals something important about modern B2B infrastructure: the companies that win aren’t necessarily those with the best features or the fastest time-to-value. They’re the ones that solve the full adoption equation.

For self-service platforms targeting enterprises, security isn’t optional infrastructure—it’s the foundation that makes everything else possible. Without enterprise-grade security, self-service just means SMB. With it, self-service can mean Fortune 500 adoption without enterprise sales cycles.

The investment required is substantial. Building SOC 2 Type 2 compliance, comprehensive data governance, and granular access controls takes engineering resources that early-stage companies struggle to afford. But the alternative is worse: building a product that either can’t serve enterprises or requires expensive sales and implementation processes to do so.

For B2B founders facing similar tradeoffs, Ofer’s approach offers a blueprint: invest in security infrastructure early, build it into your architecture rather than bolting it on later, and maintain self-service experience even as security requirements grow. The upfront cost is real, but the GTM advantage it creates is sustainable.

Tomato AI proved that self-service and enterprise security aren’t mutually exclusive. They’re complementary—when you build them right from the start.