/case-studies / logistics-agentic-routing
AGENTIC AI & AUTOMATION

An autonomous routing agent reduces failed deliveries

client
Last-Mile Carrier
industry
Logistics
services
Agentic AI · Data
duration
5 months
fig.80// skillikzmodeltraininfervectorAImodel.evalrollout82%98%accuracyusage82coveragelive
// OVERVIEW

Last-Mile Carrier — a logistics organisation — engaged Skillikz on autonomous routing agent: Predictive, self-adjusting routing for a last-mile carrier. This case study sets out the business challenge, the AI-led approach we took, the technologies involved and the measurable outcomes delivered over 5 months.

// TECHNOLOGIES
LangGraphClaude / GPT-4PythonVector DBAWSKubernetes
-22%
failed deliveries
real-time
re-routing
cost per drop
on-time rate
01 // THE CHALLENGE

Static route plans broke down against real-world traffic, access issues and customer availability, driving up failed deliveries and cost.

02 // OUR APPROACH

We built an agent that continuously re-plans routes from live signals, acting within guardrails set by operations.

Live ingestion of traffic and delivery signals
Agentic re-routing within operational guardrails
Driver app integration and feedback loop
Continuous evaluation against KPIs
03 // THE RESULTS

Failed deliveries dropped 22%, with lower cost per drop and a higher on-time rate as routes adapt in real time.

Routes now adapt to reality instead of fighting it. Drivers and customers both feel the difference.

Operations Director · Last-Mile Carrier
// HOW WE'D DELIVER THIS TODAY

The AI services behind this outcome

A project like this draws on a focused set of Skillikz services — from first assessment to a working pilot and a clear path to scale.

// MORE WORK
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