Documentation
Comprehensive guides, tutorials, and API references for NeuraNote
Quickstart
Get started in 5 minutes
API Reference
Complete endpoint docs
Guides
In-depth tutorials
Getting Started
Quick Start Guide
Get up and running in 5 minutes
Account Setup
Configure your workspace and preferences
Uploading Documents
Learn how to add and process files
Understanding AI Features
Overview of notes, flashcards, and quizzes
Features
AI Editor
Advanced editing with AI assistance
Document Processing
How NeuraNote extracts and indexes content
AI-Generated Notes
Creating comprehensive study notes
Flashcards & SRS
Spaced repetition system explained
Quiz Generation
Automatic quiz creation from your materials
AI Tutor
Interactive chat with context from your docs
Audio Transcription
Convert lectures to searchable text
Text-to-Speech
Listen to your notes on the go
AI & Technology
How RAG Works
Understanding Retrieval-Augmented Generation
Citation System
How we ensure accuracy with sources
Vector Search
Semantic search across your documents
Embedding Generation
Technical details on content indexing
AI Models Used
GPT-4, GPT-4o-mini, and other integrations
API Reference
Authentication
API key management and security
REST Endpoints
Complete API endpoint documentation
Webhooks
Event-driven integrations
Rate Limits
Understanding API quotas
Code Examples
Sample implementations in multiple languages
Integrations
Anki Export
Sync flashcards with Anki
Notion Integration
Export notes to Notion
Google Drive
Import files from Drive
Zapier
Automate workflows (coming soon)
Security & Privacy
Data Encryption
How we protect your content
Privacy Policy
What data we collect and why
Compliance
GDPR, CCPA, and education standards
Data Deletion
Removing your account and data
System Architecture
NeuraNote uses a hybrid architecture that combines serverless control plane (Next.js on Vercel) with containerized workers for heavy compute tasks. This ensures low latency for user interactions while maintaining cost-effective scaling for AI processing.
Control Plane (Serverless)
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Queue Layer (Redis)
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Data Plane (Docker Workers)
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Storage (MongoDB + R2)All AI responses use Retrieval-Augmented Generation (RAG) to ground answers in your uploaded documents. We combine vector embeddings with metadata filtering for hybrid search, ensuring accurate and cited responses.