Logistics Engineering
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.
client
LogiStream Inc.
industry
Logistics
duration
10 Months
team Size
12 Specialists
investment
$950K+
Empirical Data
Measurable Impact at Scale.
30%
Fuel Cost Reduction
Fuel expenditure reduced by 30% through intelligent route optimization algorithms.
12k Tons
CO₂ Saved Annually
Equivalent to removing 2,600 cars from the road every year.
22%
Faster Deliveries
Average delivery time improved by 22% across all regional hubs.
3.1x
Fleet Utilization
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.
Rs
Rust
Fl
Flink
Py
Python
Kf
Kafka
Pg
PostGIS
K8
K8s
Gc
GCP
Gr
Grafana