SatBrain
AI-Powered Study Assistant transforming documents into interactive learning materials.
Context & Overview
Students and professionals struggle to digest large volumes of information quickly. SatBrain was conceived to leverage GenAI to automatically transform static documents (PDFs, Audio) into active study aids like quizzes, flashcards, and visual charts.
The Challenge
Problem Statement
Learners spend 60% of their time organizing and summarizing notes rather than actually studying, leading to inefficient retention.
Why It Matters
Reducing the 'time-to-study' enables users to focus on comprehension and recall, significantly improving learning outcomes.
Constraints
- Zero-budget for infrastructure
- Need for high-accuracy summaries (low hallucination)
- Real-time processing latency
Goals & Metrics
Objectives
- Automate the creation of study materials from raw files.
- Provide visual insights (charts) from text data.
- Ensure sub-5-second response time for AI interactions.
Target KPIs
- User retention rate
- Documents processed per user
- Quiz completion rates
Research & Discovery
Methodology
Key Insights
- Students value 'visuals' (charts) almost as much as text summaries.
- Flashcards are the #1 requested feature for retention.
- Audio transcription is a key differentiator for lecture recordings.
Approach & Strategy
Vertical integration of AI services: Use Gemini 1.5 for its large context window to handle entire textbooks in one pass.
Frameworks
RAG (Retrieval Augmented Generation), Component-Driven Design (Radix UI)
Collaboration
Direct feedback loop with beta testers for rapid feature iteration.
The Solution
A comprehensive web platform where users upload content and receive a tailored study dashboard. It includes a document processor, chart generator, and an interactive quiz engine.
App Visuals
Design Rationale
Chosen Tech Stack (Supabase + React + Gemini) allowed for rapid prototyping with enterprise-grade auth and database features out of the box.
Execution
Roadmap
Phase 1
Doc upload & Summary
Phase 2
Quiz & Flashcards
Phase 3
Visual Charts & Audio
Challenges Overcome
- ⚠ Handling large PDF files (chunking strategy).
- ⚠ Ensuring consistent JSON output from LLM for charts (implemented robust validation middleware).
Outcomes & Impact
Quantifiable Results
- 1.Processed 500+ documents in beta
- 2.Reduced study prep time by ~70%
- 3.90% positive feedback on 'Visual Charts' feature
Qualitative Feedback
"Users reported 'feeling more prepared' for exams."
"Praised the clean, distraction-free UI."
Learnings & Future
Key Takeaways
- GenAI is powerful but requires strict guardrails for structured data.
- Visuals stickier than text.
Next Steps
- Mobile app development
- Collaborative study groups
- Integration with Canvas/LMS