Most "AI tutor" apps are a chat window bolted onto a system prompt. We wanted something that actually holds state — knows what you've mastered, adapts difficulty, remembers you tomorrow — across five very different domains: a general subject tutor, exam prep for India's JEE/NEET/UPSC (each with its own official syllabus and negative-marking rules), a language-learning track that goes CEFR A0 through C1, an AI career coach with mock interviews, and a competitive "arena" mode.
That range turned out to be the actual engineering problem. Not "can an LLM explain photosynthesis" — every model can do that — but: what happens when you have 60+ distinct AI-backed features, each with different cost/quality tradeoffs, and one of them starts failing at 2am?
The thing that actually mattered: a feature registry, not a prompt library
Early on, model calls were scattered — each feature picked its own model, its own retry logic (often none), its own idea of what "thinking budget" meant. A cost audit found thinking tokens (billed at the output rate) were the single largest leak in the whole system. That's what forced the redesign.
Now every AI-backed feature is one entry in a single registry file:
