I have a degree in Microbiology.

Not Computer Science. Not Software Engineering. Microbiology — the study of the smallest living things.

And yet here I am, building technology businesses, writing serverless APIs, and deploying static sites to a global edge network.

This is the story of how that happened, and why I don’t think it was an accident.

The Starting Point

At the University of Benin, I studied Microbiology. The work fascinated me — the invisible systems that determine everything from disease to fermentation to ecosystems. But I found myself increasingly drawn to a different angle: how do we understand these systems?

The answer, of course, was data. Massive amounts of it. Genomes. Expression profiles. Protein interactions. Thousands of numbers describing a single cell’s state.

And the more I looked at data, the more I realized I wasn’t really a microbiologist. I was a systems person who happened to be looking at biology.

Learning to Compute

I taught myself Python. Then SQL. Then enough statistics to be dangerous. I started building small pipelines — scripts that took raw data from one source, cleaned it, and produced something interpretable.

That’s when it clicked. Biology is a data problem. The questions are biological; the answers are computational.

I built a cancer biomarker discovery platform. Then a microbiome analysis pipeline. Then an epidemiological surveillance tool. Each one taught me the same lesson in a different way: the ability to structure, transform, and reason about data is the same skill whether the data describes a cell or a customer.

From Research to Systems

Somewhere around my third research pipeline, I realized I was no longer a biologist who coded. I was an engineer working in biology.

That shift changed everything.

I started asking engineering questions:

  • How does this system scale?
  • What happens when the data is 100× larger?
  • How do I make this reproducible?
  • How do I make this fast?
  • How do I make this deployable?

And I realized those questions had the same answers regardless of domain — patterns that repeat across every kind of system.

The Leap to Business

PrimeShieldHub began as a business need — commerce for premium gadgets in Nigeria. But the moment I tried to solve it with software, I found the same problems I’d seen in research:

  • Data integration across sources
  • Reliable state management
  • Payment reconciliation (the business equivalent of transaction logging)
  • Trust and auditability
  • Real-time visibility

The same skills. Different domain.

I built PrimeShieldHub on Cloudflare Workers. Then BuildMart, its enterprise sibling. Then Savflux, this lab.

What I Actually Learned

Microbiology didn’t teach me to write code. It taught me something more useful: how to think in systems.

A cell is a system. A business is a system. A serverless API is a system. A design system is a system. Each has:

  • Inputs and outputs
  • Feedback loops
  • Failure modes
  • Emergent behavior
  • Boundaries and interfaces

Once you’ve learned to see systems — really see them — the domain almost doesn’t matter. You can pick up the specifics of any field in months. What you can’t pick up in months is the ability to reason about structure.

The Next Chapter

Savflux is where this trajectory becomes explicit. Software. AI. Data. Cloud. Digital systems. Research.

It’s not a departure from microbiology — it’s a continuation of the same instinct. The instinct to look at a complex thing and ask: how does this actually work, and how could it work better?

That instinct works in every domain. That’s the point.