Ell-ena

Ell-ena is your AI-powered teammate that makes managing work effortless. From automatically creating tickets to capturing every detail in meeting transcriptions, Ell-ena keeps the full context of your projects at its fingertips—so nothing ever falls through the cracks.

It’s like having a smart, proactive teammate who anticipates what you need, organizes your workflow, and helps you stay on top of everything… without you even asking.

🌟 Project Vision

Imagine a world where staying productive is easy and smart. Instead of juggling different apps for tasks, tickets, and meeting notes, users can simply talk to Ell-ena – and it takes care of the rest.

Ell-ena understands natural language commands and turns them into structured tasks, tickets, or notes with context-aware automation. Whether you're a developer, student, or manager, Ell-ena fits right into your workflow and grows with your needs.

🏗️ Technical Architecture

Ell-ena implements a sophisticated architecture that combines Flutter for cross-platform UI with Supabase for backend services, enhanced by AI-powered processing pipelines for natural language understanding and contextual intelligence.

┌─────────────────────────────────────────────────────────────────────────┐
│                           FRONTEND (Flutter)                            │
├───────────────┬─────────────────┬────────────────────┬─────────────────┤
│  Auth Module  │  Task Manager   │  Meeting Manager   │  Chat Interface │
└───────┬───────┴────────┬────────┴──────────┬─────────┴────────┬────────┘
        │                │                   │                  │
        ▼                ▼                   ▼                  ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                        Supabase Service Layer                           │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  ┌─────────────┐   ┌─────────────┐   ┌─────────────┐   ┌─────────────┐  │
│  │ Auth Client │   │ Data Client │   │Storage Client│  │ RPC Client  │  │
│  └──────┬──────┘   └──────┬──────┘   └──────┬──────┘   └──────┬──────┘  │
│         │                 │                 │                 │         │
└─────────┼─────────────────┼─────────────────┼─────────────────┼─────────┘
          │                 │                 │                 │
          ▼                 ▼                 ▼                 ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                          BACKEND (Supabase)                             │
├───────────────┬─────────────────┬────────────────────┬─────────────────┤
│ Authentication│  PostgreSQL DB  │  Object Storage    │  Edge Functions │
└───────┬───────┴────────┬────────┴──────────┬─────────┴────────┬────────┘
        │                │                   │                  │
        ▼                ▼                   ▼                  ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                       AI Processing Pipeline                            │
├───────────────┬─────────────────┬────────────────────┬─────────────────┤
│ NLU Processor │ Vector Database │ Embedding Generator│  AI Summarizer  │
└───────────────┴─────────────────┴────────────────────┴─────────────────┘

✨ Current Implementation

Final_Ell-ena_overview.mp4

I’ve made demo videos for Ell-ena and separated them by features. So, we can directly check out the RAG & vector search implementation or the bot transcriber. please see this through the drive link. Loved building Ell-ena—would be super excited to see new ideas fixes and features coming up and getting it merged soon! 🚀

GOOGLE DRIVE

✨ Architecture of Ell-ena

✨ Key Features

• Generate to-do items and tickets using natural language commands
• Transcribe meetings and maintain full contextual notes
• Chat-based interface for intuitive and seamless user interactions
• Context-aware automation to enrich task details automatically
• RAG (Retrieval-Augmented Generation) implementation for contextual intelligence
• Multi-account login support with team management capabilities
• Real-time collaboration features across teams

✨ System Components

1. Frontend Layer (Flutter)

• Auth Module: Handles user authentication, team management, and role-based access control
• Task Manager: Processes task creation, updates, and workflow management
• Meeting Manager: Manages meeting scheduling, transcription, and contextual analysis
• Chat Interface: Provides natural language interaction with the AI assistant

2. Supabase Service Layer

• Auth Client: Manages authentication tokens and session state
• Data Client: Handles real-time data synchronization with PostgreSQL
• Storage Client: Manages file uploads and retrieval
• RPC Client: Executes remote procedure calls to Edge Functions

3. Backend Layer (Supabase)

• Authentication: Handles user identity, security, and session management
• PostgreSQL DB: Stores structured data with Row-Level Security policies
• Object Storage: Manages binary assets like audio recordings and documents
• Edge Functions: Executes serverless functions for business logic

4. AI Processing Pipeline

• NLU Processor: Processes natural language using Gemini API
• Vector Database: Stores and retrieves semantic embeddings for context-aware searches
• Embedding Generator: Creates vector embeddings from text for semantic similarity
• AI Summarizer: Generates concise summaries of meeting transcriptions

Data Flow

1. User Input Processing:

   • User interacts with the Flutter UI
   • Input is processed by the appropriate manager module
   • Requests are routed through the Supabase Service Layer

2. Backend Processing:

   • Authentication verifies user identity and permissions
   • PostgreSQL handles data persistence with real-time updates
   • Edge Functions process complex business logic

3. AI Enhancement:

   • Natural language is processed through the NLU pipeline
   • Text is vectorized for semantic understanding
   • Context-aware responses are generated based on historical data
   • Meeting transcriptions are summarized and enriched with action items

4. Response Delivery:

   • Processed data is returned to the frontend
   • UI updates in real-time through Supabase subscriptions
   • User receives intelligent, context-aware responses

🚀 Getting Started

Prerequisites

• Flutter SDK (3.7.0 or later)
• Supabase account
• Gemini API key
• Vexa API key

Installation

1. Clone the repository

   git clone https://github.com/yourusername/Ell-ena.git
   cd Ell-ena

2. Set up backend (Supabase)

   • Follow instructions in BACKEND.md

3. Set up frontend (Flutter)

   • Follow instructions in FRONTEND.md

📁 Project Structure

Backend Structure

supabase/
├── config.toml                # Supabase configuration
├── functions/                 # Edge Functions
│   ├── fetch-transcript/      # Retrieves meeting transcriptions
│   ├── generate-embeddings/   # Creates vector embeddings
│   ├── get-embedding/         # Retrieves embeddings
│   ├── search-meetings/       # Performs semantic search
│   ├── start-bot/             # Initializes AI assistant
│   └── summarize-transcription/ # Generates AI summaries
└── migrations/                # Database migrations

Frontend Structure

lib/
├── main.dart                  # Application entry point
├── screens/                   # UI screens
│   ├── auth/                  # Authentication screens
│   ├── calendar/              # Calendar view
│   ├── chat/                  # AI assistant interface
│   ├── home/                  # Dashboard screens
│   ├── meetings/              # Meeting management
│   ├── onboarding/            # User onboarding
│   ├── profile/               # User profile
│   ├── splash_screen.dart     # Initial loading screen
│   ├── tasks/                 # Task management
│   ├── tickets/               # Ticket management
│   └── workspace/             # Team workspace
├── services/                  # Business logic
│   ├── ai_service.dart        # AI processing service
│   ├── meeting_formatter.dart # Meeting data formatter
│   ├── navigation_service.dart # Navigation management
│   └── supabase_service.dart  # Supabase integration
└── widgets/                   # Reusable UI components
    └── custom_widgets.dart    # Shared widgets

SQL Structure

sqls/
├── 01_user_auth_schema.sql    # User authentication schema
├── 02_user_auth_policies.sql  # Row-level security policies
├── 03_task_schema.sql         # Task management schema
├── 04_tickets_schema.sql      # Ticket management schema
├── 05_meetings_schema.sql     # Meeting management schema
├── 06_meeting_transcription.sql # Transcription storage
├── 07_meetings_processed_transcriptions.sql # Processed text
├── 08_meetings_ai_summary.sql # AI-generated summaries
├── 09_meeting_vector_search.sql # Vector search capabilities
└── 10_generate_missing_embeddings.sql # Embedding generation

Future Enhancements

1. Multi-language support: Expand NLU capabilities to support multiple languages.
2. Enhanced analytics: Use AI to generate predictive analytics for tasks and meetings.
3. Offline capabilities: Allow limited offline task management with later synchronization.
4. Third-party integrations: Integrate with external productivity tools like Jira, Trello, and Google Calendar.

🤝 Contributing

Ell-ena is an open-source project under AOSSIE for GSoC'25. We welcome contributions from the community!

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please read our Contributing Guidelines for more details.

📚 Documentation

• Backend Setup: Instructions for setting up the Supabase backend
• Frontend Setup: Instructions for setting up the Flutter frontend

🎨 Figma Designs

Reference designs for the project can be found here:

• Figma Workspace

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Note: This project is part of GSoC'25 under AOSSIE and is actively under development.