This engagement predates 5 Stars Technology’s AI practice and is included to show the scale and discipline behind our work.
The situation
Education-technology platforms serve institutions that depend on them every day: enrolment, courses, records and support all run through them. They integrate with many other systems, carry sensitive student data, and have to keep working through peak periods such as the start of a term.
At that scale, architecture decisions have long consequences. A shortcut taken to ship a feature becomes a maintenance cost, an integration problem or a reliability risk for every institution that uses the platform.
What we built
Klaus worked as a .NET solution architect at Anthology, designing solutions for learning platforms. The work covered solution design, integration with surrounding systems, and the engineering standards that let a large product evolve without losing reliability.
The emphasis was on delivery discipline: clear designs, deliberate trade-offs, and solutions that the teams who inherit them can understand, run and extend.
What changed
Solutions were designed to integrate cleanly, to be maintained by the teams that own them, and to hold up under institutional scale and peak load.
Why it matters now
Every AI-assisted process we build is, first, a piece of enterprise software. It has to integrate with the systems you already run, protect the data it touches, and be handed over to a team that can operate it. The architecture discipline learned on platforms at this scale is what makes that hand-over real.
It is also why our education and technology work begins with the systems of record — admissions, student and ticketing systems — rather than around them.