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Introducing AI into a large organization brings questions that extend beyond what the technology can do. Leaders also need to understand which information it can access, who is responsible for its actions, and whether an investment improves operations across departments. Those questions become more consequential when a new application must work within established systems, approval processes, and compliance requirements.

The gap between individual benefits and broader financial results illustrates that challenge. According to McKinsey's 2026 State of AI survey, 80% of respondents said AI had improved their own productivity, while 37 percent attributed at least some impact on their organization's earnings before interest and taxes to AI. For enterprise leaders, that distinction raises questions about how improvements are measured across a business.

These concerns shape the work of Hypermodern AI, a software business founded by Dom Jocubeit, its chief engineer. Jocubeit explains the platform as an approach to building business applications with controls over data access, AI activity, and accountability incorporated into their underlying architecture. Its intended audience is enterprises seeking greater visibility into how AI operates within their organizations.

Jocubeit traces the approach to his work in privacy and enterprise technology, including banking and government projects. His initial interest concerned how information could be protected where it was stored, with clear limits on who could access it and for what purpose. As AI became part of that work, those questions expanded to include what automated systems should be permitted to do.

"We need to actually control what it can access and what it can do at that very core layer where it's running," he says.

For customers, the application-building process begins in the platform's Studio environment. Jocubeit explains that an AI assistant helps users describe the application they need and develop a schema, a structured definition of its data, functions, and interface. The platform then uses that definition to assemble the application from existing software components. "It doesn't actually write the software," Jocubeit says. "What it does is help you define a schema."

That distinction is central to the business's approach. According to Jocubeit, the application draws on code that has already been written, tested, and documented. AI helps translate the user's requirements into a definition the platform can process. Users can review the application as it develops, while documentation gives engineers information about how it works.

The Studio also supports development, staging, and production environments, which Jocubeit describes as part of managing an application's progression into operational use. Modules cover functions including billing, purchasing, inventory, and project management. Depending on the application's requirements, its interface can include conventional screens, chat, or voice interaction.

Alongside application development, Jocubeit places emphasis on traceability. He says Hypermodern signs database records to help identify changes or tampering and produces audit records that users can query. The intended purpose is to make activity easier to examine when an organization needs to establish what happened within a system.

He connects that visibility to assessing business value. In a hypothetical example, a sales assistant might reduce the time employees spend answering customer questions while creating additional work for engineers or marketing staff. Jocubeit explains that evaluating the initiative requires accounting for those connected demands. "Hypermodern is designed to bring relevant inputs and outputs together to support that assessment," he says.

Its current commercial experience remains focused. "I was testing the commercial version with two farming businesses in Australia," Jocubeit says. "That work involved making operational data more accessible and helping users make decisions." He presents those projects as early applications of a platform intended for use across industries.

The longer-term ambition is to bring Hypermodern into larger enterprises, supported by partners who understand their operational requirements. For Jocubeit, that means keeping human responsibility visible as applications take on more work. The platform's broader purpose rests on helping organizations understand both the actions their systems take and the people responsible for them. "Human accountability has to remain clear as AI takes on more responsibility," he says. "We need to understand what a system has done and who is responsible for its actions."