Data Foundations for the AI Era

In 15 years of engineering, I’ve learned that the most expensive technical debt isn't bad code—it’s unreliable data foundations.

Most growth-stage startups today are "drowning in options". They have the AI pilots and the modern stacks, but they often lack the architectural discipline to turn that data into a competitive moat.

I’ve spent my career solving these challenges within high-compliance and high-scale environments. My background includes building foundations for global leaders in Healthcare (Roche, Novo Nordisk), Energy (Equinor), and Telecommunications (Tele2). I’ve architected these solutions across on-premises, Microsoft Azure, and AWS environments, ensuring reliability where data fidelity is non-negotiable.

Solving the data foundation problem for growth-stage startups

My approach focuses on avoiding the hype cycle and balancing rapid innovation with the production stability required to scale. My goal is to ensure your architecture is actually ready for what's coming next—without the "Pilot Sprawl" that stalls so many AI initiatives.

As a Fractional CTO, I don't just "build features". I partner with leadership to:

🔹 Audit the Data Foundation
Identifying the structural bottlenecks that stop AI from being executable.

🔹 Enforce Data Contracts
Moving from "centralized bottlenecks" to a culture of ownership.

🔹 Modernize Gradually
Avoiding the "Big Bang" failures by using API-driven, phased migrations.

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Ready to audit your Data Foundation?
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