Ten Years Building Data Infrastructure for Companies That Could Afford It. Now I Build It for the Ones That Couldn't.

For ten years, I built data infrastructure at STARSchema — an 80-person data consultancy running delivery across four locations. Enterprise clients. Big budgets. Teams dedicated to nothing but data engineering, reporting, and analytics. Those companies had data problems too, but they could always afford to solve them.

Then I joined a private clinic in Budapest as Head of Development — fifty employees, sixty-plus subcontractor doctors, and processes that hadn't kept pace with the clinic's growth. No enterprise budget. No dedicated team. Just a business that needed the same thing the big companies needed — reliable data — without the resources they had to get it.

Over three years, I built it anyway. Google Workspace migration. Automated invoice capture straight from the Hungarian tax authority. A commission engine that replaced hand calculations at the reception desk. Cash and cashier reconciliation across seven registers and two locations. Inventory tracking by barcode scan. Reporting doctors and management actually check every week, because it's finally accurate. Six systems, one platform, still running today.

That's the gap DataStuds exists to close. Good data infrastructure was priced for enterprises. AI has changed the economics — what used to require a data engineering team and a six-figure budget can now be done leaner, faster, and within reach of a twenty-person business. Not by skipping the hard part, but by doing it properly at a scale that was never affordable before.