Adaptive Learning Platform for an EdTech Startup 

This seed-funded startup came to us with pilot agreements at a dozen schools and a deadline set by the academic calendar: miss the term start and the pilots slip a full year. Their product thesis was exam prep that adapts to each student, delivered over networks where a 3MB page load is a real cost to the family paying for the data.

The founders had strong pedagogy and no engineering team. We were the engineering team.

Five of us took the project from whiteboard to first pilot in sixteen weeks. The bandwidth constraint drove the architecture more than anything else: aggressive CDN caching with payloads trimmed to the kilobyte, and a client that tolerates a connection dropping mid-lesson without losing the student's progress.

Our mistake was the adaptivity engine. The first version used a model trained on data from two pilot schools, and once real students arrived it overfit badly, looping the same handful of topics for strong students because that's what the training data rewarded. We swallowed some pride in week ten and replaced it with a plainly rules-based spaced repetition system that teachers could inspect and reason about. The fancier model went back into the roadmap for the day there'd be enough data to earn it.

The platform launched on time for the term. Six months on it was serving thirty schools on the same infrastructure, and the progress dashboards had become the feature teachers mentioned first in feedback, ahead of the adaptivity that got everyone funded.