Scrubly — an AI exam-prep app for doctors on iOS & Android
We designed and built Scrubly, an exam-prep app for doctors sitting FCPS, USMLE, PLAB and DOH. It turns every practised MCQ into weak-area analytics and targeted mock papers, with a Gemini-powered AI tutor that explains answers. Flutter for iOS and Android, on a Laravel backend and admin panel.
The brief
Doctors were drilling thousands of MCQs with no idea which topics were costing them marks. Scrubly had to turn every answer into feedback.
Doctors preparing for postgraduate exams in Pakistan and abroad (FCPS, USMLE, PLAB and the Gulf DOH licensing exam) mostly study from static PDF question banks and printed books. Those give no feedback loop. A candidate can answer a thousand MCQs without learning which topics are dragging their score down, why a wrong answer was wrong, or how they compare with everyone else sitting the same paper.
Scrubly wanted one app to close that loop. It had to cover four different exam tracks, turn every attempted question into data, explain answers on demand, and let students ask open clinical questions the way they would ask a senior colleague. Behind it the Scrubly team needed an admin panel to load question banks, run subscriptions and moderate a community chat, without an engineer in the loop for day-to-day work.
The limits of what this study can say: the screenshots come from a GOFTECH test account, so the numbers visible in them (accuracy, rank, questions attempted) are test data, not user outcomes. We do not have audited usage, retention or revenue figures to publish, and none are claimed here.
The build
We built Scrubly as a Flutter app for iOS and Android on a Laravel 11 backend that also serves the admin panel. The app follows an MVVM structure with Provider. Every screen talks to a single REST API through one shared client, so behaviour such as retries, timeouts and session handling is consistent across roughly 60 screens.
The study loop is the core of the product. A student picks an exam path, sets up a study plan, and practises MCQs with instant right/wrong feedback. Every attempt is recorded, which powers three things: a Weak Areas view that ranks the topics costing the most marks, "Make me a Paper", which builds a mock paper weighted toward those weak topics, and an Analytics screen that shows accuracy, rank and percentile per subject against the whole cohort. A leaderboard, bookmarks, notes and study reminders sit around that loop.
AI is built in at two points, both through a server-side Gemini service so no model key ever ships in the app. "Explain with AI" sits under every question and expands into a worked explanation. "Ask Scrubly" is an open chat tutor that returns structured, high-yield answers (core concept, variations, clinical notes) rather than a wall of text. The same service drives text summarisation and flowchart generation for revision.
Around the study features we added real-time group and one-to-one chat on Laravel Reverb, Firebase push notifications, and PayFast subscriptions in three PKR tiers with a free trial on signup. The admin panel on the same Laravel codebase lets the Scrubly team manage exam categories, question banks, subscriptions, users and chat without touching code.
Architecture
One app, one API, AI on the server.
A Flutter client, a Laravel backend that doubles as the admin panel, and every AI call kept server-side.
Build timeline
Eight weeks. Four sprints.
Exam paths + question engine
FCPS, USMLE, PLAB and DOH tracks, the MCQ player with instant feedback, and question-bank management in the admin panel.
Analytics + weak areas
Attempt tracking, per-subject accuracy, rank and percentile, Weak Areas and Make me a Paper.
AI tutor
Server-side Gemini service powering Explain with AI, Ask Scrubly, summaries and flowcharts.
Community + subscriptions
Real-time group chat, push notifications, PayFast subscriptions and the free trial.
The build · by the numbers
By the numbers.
Tech we used
A small stack.
More build work.
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