Logistics Route Optimization with AI: 30% Fewer Miles, Same Deliveries

The Problem

A mid-size delivery company running 15 vehicles across a metro area was planning routes manually. Dispatchers used spreadsheets and Google Maps to assign deliveries each morning — a process that took 2 hours and rarely produced optimal routes.

Pain points:

• Drivers spending 30% more time on the road than necessary
• Fuel costs eating into margins
• Late deliveries during bad weather or traffic
• No real-time adjustment capability

What We Built

An intelligent route planning system:

• Webhook intake — receives delivery requests from the order management system
• Real-time data enrichment — pulls live weather and traffic data
• AI route calculation — optimizes for distance, time, delivery windows, and vehicle capacity
• Dynamic re-routing — adjusts routes in real-time based on traffic updates
• Dispatch automation — generates route maps, ETAs, and driver instructions
• Customer notifications — automated SMS with accurate delivery windows

Technical Architecture

Order webhook → Data enrichment (weather + traffic APIs)
→ AI route optimization (constraint solver + LLM)
→ Route assignment → Driver app notification
→ Real-time tracking → Customer SMS updates

The system processes shipment batches every 30 minutes, allowing for dynamic adjustment as new orders come in throughout the day.

Results After 90 Days

Key Takeaway

Route optimization isn't just about saving fuel — it's about making every delivery predictable. When customers know exactly when their package arrives, complaints drop, reviews improve, and repeat business grows.