Originally devised as a Python prototype, FlashcardsGPT faced three main limitations: reliance on a single machine, lack of user management, and external AI dependencies that raised privacy concerns. These gaps hindered the system’s scalability and commercial viability, preventing broad adoption in settings where multiple users, robust security, and efficient data handling were essential.

A Node.js (Express) backend and MongoDB were introduced for flexible data handling, supported by RabbitMQ to process tasks asynchronously and maintain performance. A hybrid AI solution was employed: an on-premise LLaMA model for routine inference and cloud-based endpoints for surges. Security measures included JWT/Auth0 authentication, role-based access control, and a billing foundation to accommodate subscription models.

Thanks to its new architecture, FlashcardsGPT efficiently manages concurrent requests and seamlessly scales to meet fluctuating demand. The local AI inference model protects sensitive data by avoiding constant external API calls, ensuring higher privacy standards. When usage spikes occur, the system automatically offloads tasks to cloud endpoints, optimizing resource use and costs. For the client, this design provides a robust, market-ready platform that can evolve alongside user needs. The integrated user management and subscription framework allow for flexible monetization options, making the service commercially sustainable. Furthermore, the strict data-handling protocols build user trust in educational and organizational environments. By delivering a secure, adaptive, and cost-conscious solution, FlashcardsGPT is poised to address growing requirements in AI-driven learning while offering the client a clear competitive edge.