A national government agency needed an edge AI platform to modernize surveillance, infrastructure monitoring, and field operations without relying on continuous cloud connectivity. Aeologic developed a custom NVIDIA Jetson solution that processed video, sensor, and operational data directly at the edge, enabling real-time intelligence, faster decision-making, and secure AI-powered government services across distributed locations. NVIDIA Jetson is widely used for edge AI, computer vision, and autonomous systems in public sector environments.
The authority operated 140 signalized intersections across three states with legacy inductive-loop sensors and siloed camera feeds. Traffic engineers adjusted signal timing manually based on historical patterns, and incident response relied on phone reports from field staff — often 12–18 minutes after an incident began.
Rather than replace existing sensor hardware, Aeologic layered a Sense → Decide → Act pipeline on top of it: normalizing feeds from inductive loops and traffic cameras, training SageMaker forecasting models on 14 months of historical flow data, and pushing predicted congestion windows back to the signal controllers as adaptive timing recommendations.
A GIS-based operations map gives traffic engineers a single live view of every intersection, with automatic anomaly flags and one-click rerouting suggestions during incidents.
"We went from finding out about congestion after the fact to seeing it forming twenty minutes before it happens. That's the difference between managing traffic and predicting it."
— Program Director, State Transport Operations (illustrative quote — replace with a verified client attribution)