AI-Powered Dispatch System

Reducing empty miles by 34% with intelligent routing

OBJECTIVES

Reduce empty (deadhead) miles by 25%+Optimize multi-stop routing for fuel efficiencyPredict delivery delays and proactively communicate

PROJECT TYPE

AI Assistant

A regional trucking company with 85 trucks operated on gut instinct and spreadsheets.

Optimize Your Fleet

15 MIN · NO PREP REQUIRED

Custom Python ML for route optimization—Samsara API for real-time tracking—AWS Lambda for serverless compute—PostgreSQL with PostGIS for geospatial—React Native driver app with offline sync—DAT and Truckstop API integration—

OVERVIEW

A regional trucking company with 85 trucks operated on gut instinct and spreadsheets. Dispatchers made routing decisions based on experience, not data. Empty return trips ate into margins. We built an AI dispatch assistant that optimizes routes, predicts delays, and matches loads for maximum efficiency.

THE PROBLEM

The company's three dispatchers juggled 85 trucks across six states. They used a transportation management system for basic tracking, but routing decisions happened in their heads.

Experience-based dispatching worked, but it didn't scale. When a senior dispatcher retired, years of route knowledge left with him. Empty miles averaged 28%—meaning more than a quarter of all miles driven generated zero revenue.

Drivers complained about inefficient routing. Fuel costs climbed. Meanwhile, competitors using modern logistics software won contracts with faster delivery times and lower rates.

The company needed to encode expert knowledge into a system that could optimize at a scale no human dispatcher could manage manually.

CONSTRAINTS

Must work with existing Samsara TMS
Drivers have varying technology comfort levels
Cannot require internet connectivity in rural areas
Must account for HOS (Hours of Service) regulations
Real-time replanning when delays occur
Dispatchers must understand and trust AI recommendations

DELIVERABLES

What we shipped.

FIG. 02

AI dispatch assistant with route optimization

Load matching engine for return trips

Delay prediction with automatic customer notification

Samsara TMS integration for real-time tracking

Driver mobile app with offline capability

Dispatcher dashboard with AI explanations

HOS compliance validation in routing

KEY DECISIONS

How we solved it.

FIG. 03

Replace dispatchers or augment them?

Augment with AI recommendations

Dispatchers have relationship knowledge AI can't replicate—driver preferences, customer quirks, local conditions. AI provides optimized options; dispatchers make final calls. Trust came from transparency.

Historical data or real-time optimization?

Hybrid with continuous learning

Historical patterns train the model (traffic by time, seasonal demand, driver performance). Real-time data adjusts routes mid-trip. The system learns from every deviation between predicted and actual times.

Internal load board or external integration?

Both with priority scoring

Internal loads get priority, but AI also monitors DAT and Truckstop load boards for return trip options. Priority scoring considers rate, location, timing, and driver preferences.

OUTCOMES