GyanVerse
Link to open source: https://raunaksri30.github.io/gyanverse1/
Link to Live Project: https://raunaksri30.github.io/gyanverse1/
The proposed solution is an AI-powered unified study companion that replaces the need for multiple scattered learning apps and tools. Instead of students switching between notes, Google searches, YouTube, reminder apps, textbooks, and separate doubt-solving platforms, this system brings everything into one intelligent space. Students can upload their notes, books, PDFs, lecture files, or web links, and the AI will use Retrieval-Augmented Generation (RAG) to answer their questions only from their own study material, ensuring accuracy, relevance, and zero hallucination. It will automatically organize study resources into topic-wise searchable formats, create revision summaries, and even generate flashcards and quizzes from the uploaded content.
The platform includes built-in assignment planning, reminders, and personalized study schedules so students never miss deadlines and always know what to study next. It tracks academic progress using machine learning, identifies weak areas, and suggests targeted revision. A gamified recall system—XP points, streaks, badges, quizzes, spaced repetition—keeps students motivated and increases long-term memory retention. Users interact with the system through a chat-based interface and dashboard, accessible via web or mobile, and activities like reminders, calendar sync, and content extraction will be automated through n8n workflows.
The solution is unique because it combines AI doubt-solving, study material management, revision enhancement, analytics, and task planning in one platform, something no existing chatbot or EdTech tool does holistically. It will work because it solves a real everyday problem: students don’t lack content, they lack structure, clarity, recall, and personalized academic support. This tool acts not just as a chatbot, but as a digital study brain—organizing, teaching, tracking, and guiding the student continuously.
This build was uploaded as a hackathon project


