Jeffito is a complete and innovative educational platform, designed to support Mozambican students in creating high-quality academic assignments. The platform simplifies the process by offering structured document templates, proper referencing, and study tools, enhancing both productivity and educational outcomes.
With intelligent academic work generation, the system creates structured content using natural language processing, automatically organizing by themes and topics. The assignments include bibliographic references in ABNT and APA formats, ensuring compliance with academic standards. The system also offers automatic topic generation from images, utilizing image recognition to facilitate work creation.
Jeffito offers a complete system of interactive quizzes with automatic question generation based on created works, allowing students to test their knowledge. The platform includes a flash card system for efficient study, with spaced repetition algorithms to optimize memorization. The flexible subscription system allows monthly plans or pay-as-you-go usage, with direct M-Pesa integration for simplified payments.
In addition, the platform features a modern interface with integrated chat for real-time feedback, an influencer system with commissions, and prize management. Built for educational digital transformation, Jeffito represents the future of academic assistance in Mozambique, with an ongoing improvement process to strengthen its credibility and local market presence.
This platform was built with modern technologies, following best practices for scalability and maintainability.
Backend Architecture
The backend follows SOLID principles and a modular architecture, ensuring clean code and easy extensibility. We implemented rigorous Test-Driven Development (TDD) to maintain high code quality and reliability.
Core Technologies:
- TypeScript for static typing and enhanced development safety
- PostgreSQL for robust data persistence
- Prisma as the ORM for type-safe database access
- Redis for efficient caching and performance optimization (chat history)
- AI SDK and OpenAI for LLM-powered text processing
- NestJS as the web framework for building RESTful APIs
- Docker and Docker Compose for containerization and orchestration
- Jest as the testing framework to ensure code quality and coverage
- M-Pesa API for mobile payment processing
Key Features:
- Intelligent chat functionality for real-time user interaction
- Automatic academic work generation with bibliographic references
- Multi-level authentication system (Admin, Influencer, User)
- Automatic topic generation from images using image recognition
- Interactive quiz system with automatic question generation
- Flash cards with spaced repetition algorithm
- File upload and cloud storage
- Transactional email delivery
- Subscription and pay-as-you-go system
- M-Pesa integration for mobile payments
- Commission system and influencer management
- Work export to DOCX format
Frontend Architecture
The frontend uses Feature-Sliced Design (FSD) architecture, which enabled smooth evolution of the platform over time and facilitated team collaboration through clear separation of concerns.
Tech Stack:
- React for building interactive user interfaces
- TypeScript for type safety and scalability
- Vite for fast development and optimized builds
- Tailwind CSS v4 for modern and responsive styling
- Shadcn UI for accessible, headless UI components
- TanStack Query (React Query) for powerful async data management
- TipTap for rich text and markdown editing
- Framer Motion for fluid animations
- PWA capabilities for offline functionality and app-like experience
- Playwright for end-to-end testing
This architectural approach allowed the platform to scale effectively while maintaining code quality and developer productivity.