AI optimization platform replaces manual route planning with automated, constraint-aware delivery planning.
- Deployment status
- Beta
- Planning automation
- Fully automated
- Scalability
- High
What we shipped
An optimization engine that assigns packages to drivers and vehicles, generates routes under operational constraints, and scales logistics planning.
Transportation and logistics companies run complex delivery networks where drivers, vehicles, packages, and locations must be coordinated continuously.
Blueprint → AI Pilot → Production launch → Scale and operate.
We followed the Datablooz Delivery Model. See our process.
- Blueprint
Captured operational inputs, constraints, and rules that the optimization engine must respect.
- AI Pilot
Prototyped vehicle routing and scheduling algorithms on a real delivery dataset, benchmarked against manual plans.
- Production launch
Delivered the logistics optimization engine with centralized planning UI and configurable parameters.
- Scale and operate
Added demand forecasting, dynamic fleet allocation, predictive maintenance, and real-time traffic re-planning.
Business, technical, and governance outcomes.
- Automated daily route generation.
- Better balanced driver workloads.
- Reduced unnecessary travel.
- Scalable platform across delivery scenarios.
- Python
- OR-Tools
- FastAPI
- PostgreSQL
- Redis
- Mapbox
Configurable optimization parameters per company, logged plan versions, and data pipelines prepared for future ML enhancements.
Working on something similar?
Schedule a call. We will tell you honestly whether AI is the right move.
Reference calls available under NDA after the second working session.