Real-Time AI Data Platform
Live data streaming with WebSocket-powered backend
The client needed a platform that could ingest, process, and visualize data streams in real time — with AI-driven anomaly detection and alerting.
The Challenge
The client needed a platform that could ingest, process, and visualize data streams in real time — with AI-driven anomaly detection and alerting. Traditional request-response APIs couldn't deliver the sub-second latency their operations team required for monitoring critical business metrics.
Our Solution
We built a real-time data pipeline using Django Channels for WebSocket connections and Socket.io on the frontend for persistent bi-directional communication. A custom admin panel provides live dashboards with streaming charts that update as data arrives. AI-driven analytics detect anomalies in incoming data streams and trigger automated alerts via email and Slack. The platform handles thousands of concurrent connections with horizontal scaling.
The Results
Sustains 5K+ concurrent WebSocket connections with sub-100ms message delivery
AI anomaly detection catches 92% of data irregularities before human review
Reduced incident response time from 15 minutes to under 30 seconds
Technical Approach
Django Channels was chosen over standalone WebSocket servers to keep the authentication, permissions, and business logic in one framework. Redis serves as both the channel layer for WebSocket message routing and the caching layer for frequently accessed data. The AI anomaly detection model runs as a separate Celery worker to avoid blocking the main event loop.
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