Autonomous Route Intelligence.
Building a real-time spatial optimization engine that cut fuel costs by 30% and saved 12,000 tons of CO₂ annually across a global logistics network.
Empirical Data
Measurable Impact at Scale.
Fuel expenditure reduced by 30% through intelligent route optimization algorithms.
Equivalent to removing 2,600 cars from the road every year.
Average delivery time improved by 22% across all regional hubs.
Fleet asset utilization improved by 3.1x from baseline through dynamic dispatch.
Engineering Challenges
Dynamic Rerouting
Real-time path recalculation across 40,000+ active vehicles accounting for traffic, weather, and capacity constraints simultaneously.
Data at Scale
Ingesting and processing 2 billion GPS data points daily from IoT-equipped fleet vehicles without latency degradation.
Carbon Compliance
Meeting evolving EU carbon reporting mandates while maintaining operational performance across 14 country jurisdictions.
Engineering Solutions
We built a graph-based spatial intelligence engine using Rust, Apache Flink, and a proprietary multi-objective optimization solver.
- Rust-native graph traversal for sub-50ms rerouting
- Apache Flink stream processing at 2B events/day
- ML demand forecasting for proactive dispatch
- Real-time CO₂ tracking with regulatory export APIs
Technical Stack
Built for continental scale.
Every component designed for high-throughput geospatial workloads, real-time stream processing, and multi-jurisdiction compliance.