An AI-powered auxiliary monitor for 911 dispatchers that provides real-time transcription, incident analysis, and suggested responses.

• Live Call Transcription: Real-time speech-to-text using Web Speech API
• AI Incident Summarizer: Intelligent analysis using Ollama (local AI) or mock data
• Suggested Script: Dynamic dispatcher responses based on conversation context
• Smart Classification: Auto-assigns calls to Police/Fire/Medical with priority levels
• Real-world Data Integration: Uses NYC, Seattle, and NENA datasets for classification
• Call Queue & History: Track active and completed calls
• Time-to-Dispatch Timer: Measure response efficiency
• Export Functionality: Download incident data as JSON/PDF

1. Install Vercel CLI:

   npm i -g vercel

2. Login to Vercel:

   vercel login

3. Deploy:

   vercel

4. Follow the prompts:

   • Link to existing project: No
   • Project name: dispatchai-copilot
   • Directory: . (current directory)
   • Override settings: No

1. Push your code to GitHub
2. Connect your GitHub repository to Vercel
3. Vercel will automatically detect the static site and deploy

python -m http.server 8000

Then open http://localhost:8000

npx serve .

1. Install Ollama: https://ollama.ai/
2. Pull the model: ollama pull llama3.2
3. Start Ollama: ollama serve
4. The app will automatically detect and use Ollama

If Ollama is not available, the app uses intelligent mock responses based on real-world 911 data patterns.

• Chrome/Chromium (recommended for Web Speech API)
• Firefox
• Safari
• Edge

• All data is processed locally
• No data is sent to external servers (except Ollama if running locally)
• Real-world datasets are included for training classification models

MIT License - see LICENSE file for details