VisioSphere is a cloud-native immersive data visualization and accessibility platform that combines AR/VR interfaces, multimodal interaction, real-time collaboration, and Generative AI-driven analytics.

The system transforms complex multidimensional datasets into intuitive 3D interactive environments, enabling spatial exploration, AI-powered insights, and collaborative analytics.

VisioSphere is designed for education, healthcare visualization, enterprise intelligence, and creative industries.

To advance next-generation Human-Computer Interaction (HCI) by merging immersive computing, artificial intelligence, and accessibility-first design into a unified platform.

• WebXR-based immersive rendering
• Three.js-powered spatial data exploration
• Real-time transformation of structured datasets
• Interactive object manipulation (zoom, rotate, cluster)

• Voice-based querying
• Gesture-ready architecture
• Multimodal input framework
• Accessibility-first UI design

• WebSocket-based shared sessions
• Multi-user environment synchronization
• Live AI-assisted collaborative querying

• Natural language data querying
• Automated analytical summaries
• Predictive explanation synthesis
• Context-aware recommendations

• KMeans clustering
• Linear regression predictions
• Statistical anomaly detection

Frontend (React + Three.js + WebXR)
↓ REST / WebSocket
Backend (FastAPI Microservices)
↓
AI Engine + Analytics Layer
↓
Cloud Infrastructure (Docker + Kubernetes)

• React.js
• Three.js
• @react-three/fiber
• WebXR API
• Axios

• FastAPI
• WebSockets
• Pydantic
• OpenAI API

• Scikit-learn
• NumPy
• Pandas

• Docker
• Docker Compose
• Kubernetes
• Nginx (optional ingress)

visiosphere/
│
├── backend/
│   ├── main.py
│   ├── ai_service.py
│   ├── advanced_analytics.py
│   ├── websocket_manager.py
│   ├── requirements.txt
│   └── Dockerfile
│
├── frontend/
│   ├── package.json
│   ├── src/
│   │   ├── App.js
│   │   ├── VRScene.js
│   │   ├── VoiceControl.js
│   │   └── Collaboration.js
│   └── Dockerfile
│
├── k8s/
│   ├── backend-deployment.yaml
│   ├── frontend-deployment.yaml
│   └── secret.yaml
│
├── docker-compose.yml
└── README.md

• Docker
• Docker Compose
• Node.js (if running frontend separately)
• Python 3.11+

docker-compose up --build

Frontend: http://localhost:3000

Backend API: http://localhost:8000

1. Build Docker images.
2. Push images to container registry.
3. Apply Kubernetes configs:

kubectl apply -f k8s/

Backend requires:

OPENAI_API_KEY=your_api_key_here

For Kubernetes:

• Store API key in a Secret resource.
• Inject via environment variables.

GET / Returns system status.

POST /ai-insight

Body:

{
"prompt": "Explain clustering trends in dataset"
}

POST /analytics/clustering

Body:

{
"values": [[1,2],[3,4],[5,6]]
}

ws://localhost:8000/ws/{client_id}

• Stateless backend services
• Horizontal Pod Autoscaling (Kubernetes)
• Load-balanced WebSocket gateway
• Secret-based API management
• Async FastAPI event handling

• Environment-based secret management
• API key isolation
• WSS-ready WebSocket configuration
• Extendable RBAC layer
• OAuth2 integration (future extension)

• Redis-based session persistence
• PostgreSQL integration
• WebRTC for immersive collaboration
• OAuth + SSO
• Monitoring with Prometheus & Grafana
• CI/CD with GitHub Actions
• Edge inference optimization

Education:

• Immersive STEM learning environments

Healthcare:

• Medical imaging visualization

Enterprise:

• 3D business intelligence dashboards

Creative Industries:

• Spatial analytics & generative design

• Immersive AI-powered analytics
• Multimodal accessibility-first engineering
• Real-time collaborative spatial computing
• Cloud-native scalable architecture
• Enterprise-ready deployment model

Harsh Sonkar
AI Engineer | Data Scientist | Cloud & Immersive Systems Developer

This project is licensed under the MIT License.