AiruNote
AI-Assisted TXT | MD | RTF Capture & Knowledge Management System
AI-powered note capture, structured knowledge management, and document organization system. Built with privacy-first architecture and modular extensibility for enterprise knowledge workflows.
Problem Context
Organizations needed a unified system to capture, organize, and retrieve knowledge across multiple document formats (TXT, Markdown, RTF) while maintaining privacy and supporting team collaboration.
Key challenges included:
- •Fragmented knowledge across different file formats and storage locations
- •Lack of structured organization and ownership boundaries
- •Manual document management without AI-assisted capture capabilities
- •Need for modular, installable architecture within larger platform ecosystems
System Architecture
The system follows a modular, installable-app architecture with clear ownership boundaries and metadata-driven rendering. The architecture separates document lifecycle management from presentation logic, enabling extensibility without core system changes.
Core components include:
- •Folder-based organization with hierarchical structure and ownership scoping
- •Metadata-driven rendering engine supporting multiple document formats
- •AI-assisted capture workflows integrated into document creation pipeline
- •Privacy-first data isolation ensuring user data remains secure and private
Technical Stack
My Contributions
Hands-on contribution within a team environment. Key responsibilities included:
- •Designed structured document lifecycle architecture with clear state transitions
- •Implemented folder hierarchy and ownership boundary logic in backend services
- •Built installable-app modular system enabling platform integration
- •Created metadata-driven rendering engine supporting TXT, MD, and RTF formats
- •Integrated AI-assisted capture workflows with secure API connections
- •Developed React components for document editing, folder navigation, and search
Implementation Highlights
Notable implementation details:
- •Save-before-edit pattern preventing data loss during document modifications
- •Optimistic UI updates with rollback capabilities for responsive user experience
- •Hierarchical folder navigation with efficient data loading and caching strategies
- •Format-specific rendering logic with preview capabilities for Markdown documents
- •Privacy-first architecture ensuring user data isolation and security
Outcome & Impact
The system successfully addresses knowledge management challenges:
- •Unified document capture and organization across multiple formats
- •Improved knowledge retrieval through structured folder organization and search
- •Enhanced productivity with AI-assisted capture reducing manual data entry
- •Modular architecture enables seamless integration within larger platform ecosystems
- •Privacy-first design ensures secure handling of sensitive organizational knowledge
Module Breakdown
Client identity anonymized due to confidentiality agreements.