Predictive Systems for Smart Cities
Neurom's predictive analytics engine powers city-level digital twins, integrating IoT sensors, environmental data, and AI forecasting to enhance urban sustainability and citizen safety.
Problem Statements
- Urban infrastructure systems operate in silos — power grids, traffic, utilities.
- Poor response times to environmental or emergency events.
- Inefficient resource allocation across city services.
- Lack of predictive capabilities for infrastructure maintenance.
- Limited visibility into city-wide operational health.
Problem Solution
- Unified multiple data layers into a city-scale digital twin.
- Deployed AI-driven forecasting models for traffic and energy optimization.
- Integrated IoT sensors for real-time environmental monitoring.
- Built cross-department dashboards for coordinated response.
Client Benefits
- 30% improvement in traffic flow optimization during peak hours.
- Reduced energy consumption through predictive load balancing.
- Faster emergency response through real-time situational awareness.
- Proactive infrastructure maintenance reducing repair costs.
- Enhanced citizen safety through predictive incident detection.