Edge-to-cloud architecture processing 50M+ daily IoT events with Arabic-first AI interfaces — delivered in 4 months
A national transformation initiative needed an enterprise AI platform to process smart city sensor data while meeting strict data sovereignty requirements. Existing vendor proposals required 18+ months and lacked Arabic language capabilities.
Size: 500-5,000 employees
Build an enterprise-grade AI platform to process smart city sensor data and deliver real-time predictive analytics for infrastructure management.
Process 50M+ daily events from IoT sensors across smart building, transportation, and utility systems
Deliver sub-second predictions for energy optimization, traffic flow, and maintenance scheduling
Integrate with legacy SCADA systems and modern IoT protocols while maintaining security
Comply with Saudi SDAIA data governance requirements and prepare for international expansion
Enable Arabic-first AI interfaces while supporting multilingual operations
Design for extreme climate conditions (45°C+) with edge processing requirements
Designed and implemented a multi-layer AI platform with edge-to-cloud architecture, specialized Small Language Models for Arabic processing, and real-time analytics engine.
Built a sovereign AI architecture that keeps sensitive data within Saudi Arabia while using cloud scalability for non-sensitive workloads. Deployed specialized SLMs (fine-tuned Phi-4) for Arabic language processing at the edge, enabling low-latency responses without external API dependencies. Created a unified data platform that normalizes diverse IoT protocols into a consistent analytics layer.
Designed hybrid edge-cloud architecture meeting SDAIA data sovereignty requirements. Created unified data model for 15+ IoT protocols. Established security framework for critical infrastructure.
3 weeksDeployed edge computing nodes with custom SLM for Arabic NLP, optimized for 45°C+ operation. Built offline-capable processing for critical functions. Achieved <50ms inference latency.
5 weeksImplemented real-time streaming analytics (Apache Flink) processing 50M+ events/day. Built predictive models for energy optimization (12% reduction) and predictive maintenance (87% accuracy).
4 weeksIntegrated with existing SCADA and BMS systems. Deployed Arabic-first dashboard with RTL support. Trained local team on platform operations and model maintenance.
4 weeksDelivered a production AI platform processing 50M+ daily events with 99.9% uptime, achieving 12% energy cost reduction and 87% predictive maintenance accuracy.
“Sovereign AI is not just a compliance requirement — it is a strategic advantage. When sensitive infrastructure data stays within national borders and AI interfaces speak the local language natively, adoption accelerates and trust follows.”