Our Portfolio

Challenge

A leading global financial services firm was processing millions of transactions daily but lacked real-time fraud detection capabilities. Legacy systems had multi-second latencies, causing delayed response to fraudulent activities.

Solution

Implemented DataStreamNet to process transaction streams with sub-second latency. Integrated machine learning models for pattern detection and anomaly identification. Created real-time dashboards for fraud team monitoring.

Results

• 98% detection rate for fraudulent transactions
• Reduced fraud losses by $50M annually
• 3ms average processing latency
• 500K+ transactions processed per second
• 99.99% platform uptime

Challenge

A major e-commerce platform struggled with real-time inventory synchronization and personalized recommendations. Batch processing caused stock discrepancies and missed personalization opportunities across millions of concurrent users.

Solution

Deployed DataStreamNet to ingest user events, purchases, and inventory changes in real-time. Built streaming pipeline for personalized recommendation engine with ML model integration. Synchronized inventory across 50+ warehouses globally.

Results

• 35% increase in conversion rates
• Real-time inventory accuracy across all channels
• 2M+ events processed per second
• 50ms personalization recommendation latency
• $120M+ annual revenue lift

Challenge

A global manufacturing enterprise operated 50K+ sensors across 25 factories but couldn't analyze data in real-time. Equipment failures occurred with minimal warning, causing $30M+ in annual downtime losses.

Solution

Implemented DataStreamNet to aggregate and process sensor data from all factories globally. Built predictive maintenance models using stream processing and machine learning. Created real-time alerting for anomalies and equipment degradation patterns.

Results

• 42% reduction in unplanned downtime
• $18M+ savings in preventive maintenance
• 1M+ sensor events processed per second
• Average 2-week advance warning for failures
• Quality defect rate reduced by 28%

Challenge

A healthcare analytics provider needed to process patient vital signs from 500K+ patients across multiple hospitals. Batch processing delays caused slow responses to critical health events, putting patient safety at risk.

Solution

Deployed DataStreamNet for real-time ingestion of patient monitoring data. Implemented stream processing for anomaly detection based on clinical thresholds. Created real-time dashboards for clinical teams and automated alert systems.

Results

• 20x faster alert response for critical events
• 100ms average latency for patient data
• 500K+ patients monitored continuously
• 30% improvement in patient outcomes
• Full HIPAA compliance with data encryption