How does AI optimize delivery routes?

An optimization engine learns from your operation's history and computes the best combination of routes for each day, distributing deliveries and vehicles to cut miles driven. It's the same smart-routing logic we built into an urban mobility app with real-time geolocation.

Does routing account for traffic and delivery windows?

Yes. The route is calculated taking into account real-time congestion, time restrictions, and each customer's delivery window, plus vehicle capacity. It's the kind of real-time calculation we've already applied in an urban mobility app.

Can routes be replanned during the day?

Yes. Routing is dynamic: when a new order, a cancellation, or a traffic disruption comes up, the route is recalculated in real time to keep the operation efficient and the delivery on time.

Does optimization reduce cost and CO2 emissions?

Yes. Fewer miles driven means less fuel, fewer tolls, and lower emissions. The dashboard shows cost per route, consumption, and CO2, so you can track the real savings and meet sustainability targets.

Does the solution integrate with my TMS?

Yes. We connect the routing engine to your TMS and operational systems so optimized routes arrive ready at dispatch and in the driver's app. It's the same end-to-end integration we delivered in an urban mobility app, with payments and geolocation integrated.

AI Route Optimization · Logistics The best route, calculated for every delivery

Burn less fuel and hit the promised time with AI-driven routing that factors in traffic, delivery windows, and fleet capacity, and recalculates routes on the fly as the day changes.

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30%

fewer miles driven with smart routing (industry benchmark)

The challenge

Every poorly planned route is fuel and time lost

Traffic, delivery windows, and fleet capacity decide the cost of the operation and the customer's promised time. The gaps we see most:

Routes built on guesswork

Routing done in a spreadsheet and in the dispatcher's head can't keep up with delivery volume. Every route decided by hand is extra miles driven and time lost.

Traffic and delivery windows ignored

A route that ignores congestion, time restrictions, and the customer's window turns into a late delivery, an idle vehicle, and a customer waiting all day.

Freight costs that keep climbing

Fuel, tolls, and driver overtime weigh on the bottom line. Without optimizing the route, every price increase becomes margin that vanishes from the operation.

Underused fleet

Vehicles leaving half-loaded, idle drivers, and one truck too many on the road. Poorly distributed capacity is money running without delivering results.

How we implement

From route calculation to the cost dashboard

We build route optimization with the standards we learned shipping smart routing and real-time geolocation in production.

An AI routing engine

Every operating day starts with the best combination of routes, computed by an optimization engine that learns from your delivery history. It's the same smart-routing logic we built into an urban mobility app.

Best route by traffic, window, and capacity

The route is calculated factoring in real-time congestion, the customer's delivery window, and each vehicle's capacity. Fewer miles driven, times met, and drivers on the right road.

Multi-stop optimization

Dozens of stops sequenced in the ideal order, with load distributed across available vehicles. The operation heads out with a full fleet and the shortest possible route.

Simulation and cost dashboard

Run scenarios before dispatch and track cost per route, fuel, and CO2 emissions in a single dashboard. The decision stops being a guess and becomes a number.

Use cases

Logistics projects Luby put into production

Real projects in logistics, with the results they produced.

Modernizing a white-label ERP into a secure, high-performance web platform

38.5% usability improvement · 72% faster task completion · ~4,700h saved per month

Automotive operations case study

35% lower cost per last-mile delivery

35% reduction in delivery cost · 50% shorter delivery time · 99% successful delivery rate

Digital logistics case study

25% less time spent getting across the city

5M+ geolocation points per day · 25% reduction in average travel time · 95% accuracy of arrival estimates

Urban mobility case study

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Technologies

Technologies & partners

Python

Machine Learning

Node.js

Google Maps API

PostgreSQL

AWS

Verified by our clients, not by us

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Common questions about AI route optimization

Let's drive less and deliver more

Bring your routing and freight-cost challenge. You'll leave the conversation with a clear technical path, from the AI engine to the integration with your TMS.

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