Logistics_
21%
Fuel savings
Across 200 trucks
94%
On-time delivery
Up from 81%
-32%
Empty miles
Year over year
2.1x
First-year ROI
Payback in 7 months
The challenge
TRUCKS RAN HALF EMPTY
Redline's planners optimized routes manually every morning in spreadsheets. Empty miles kept creeping up, fuel costs were eating margin, and dispatchers were stretched thin. Their legacy TMS could not be replaced without a multi-year project the CFO would not fund.
The company needed logistics software development services that would layer AI for logistics on top of what they already had and pay for itself inside 12 months.
32%
Empty-mile rate
81%
On-time delivery
$4.8M
Annual fuel spend
The approach
FOUR PHASES
We sequenced the work into four phases so each release added AI agents for logistics in a way that was cash-flow positive and easy to adopt for planners and drivers.
Phase 1
Wks 1–6Discovery and pilot
We established baseline operations and validated value early by wrapping the existing TMS with logistics AI rather than replacing it.
Two-week site visit and dispatch sprint with planners and drivers
Dispatch audit across three hubs to baseline KPIs and cost per mile
Route optimizer prototype that wrapped the existing TMS instead of replacing it
12-truck shadow pilot to validate AI solutions for logistics against real routes
Phase 2
Wks 7–16ETA Prediction
This phase delivered live ETA intelligence and agent-driven recommendations directly into dispatcher workflows and customer visibility surfaces.
Gradient-boosted ETA model trained on 18 months of GPS and delivery history
AI agents for logistics that monitor traffic, weather, and dock hours in real time
Dispatcher UI that shows suggested changes with cost and SLA impact
Customer portal for live ETAs and proactive delay notifications
Accuracy reporting that risk and operations teams could audit
Phase 3
Wks 17–24Fleet Rollout
We prioritized adoption and operational continuity so dispatchers and drivers could use AI recommendations without losing control.
Driver app with turn-by-turn updates integrated into existing tablets
Tablet refresh and training program for all routes and shifts
Change management sessions for dispatchers focused on "copilot, not autopilot"
Daily stand-ups to review AI for logistics recommendations and override patterns
Phase 4
Wks 25–52Managed Ops
Post-launch operations tied model performance directly to business outcomes through managed support and cost governance.
Weekly model retraining from fresh GPS and delivery data
On-call dispatch support and incident response for peak seasons
QBRs on cost per mile, empty miles, and on-time performance
Cost tracker that ties logistics software development services directly to P&L
"
They did not try to replace our TMS. They sat next to it, made it smarter, and got out of the way. Our dispatchers still run the show. They just have a better copilot now, powered by AI agents for logistics instead of more spreadsheets.
RC
Ramon Castillo
COO, Redline Logistics
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