Illustrative concept project — this is not a verified client case study. The project details and outcomes shown here are not claims about completed client work.
RoutePilotAI Logistics Route Optimisation
Route optimisation for delivery fleets that cuts mileage and fuel while keeping every promise window intact.
The Project
A regional delivery operator was routing hundreds of daily stops by hand experienced but inconsistent, and impossible to scale. RoutePilot brought algorithmic rigour to the dispatch desk.
We built a routing engine that plans optimised multi-stop routes around traffic, time windows and vehicle capacity, then pushes them straight to drivers' phones with live re-optimisation as conditions change.
The difference was visible from the first week: shorter routes, lower fuel spend and happier customers.
Challenge, Solution & Results
How we approached the problem, what we built and the outcomes we're tracking.
The Challenge
Real-world routing is messy traffic, time windows, driver breaks and last-minute orders all fight the plan. The solver had to be fast enough for live re-routing yet robust to messy input.
Our Solution
A constraint-based optimisation engine (with a greedy fallback), live GPS-driven re-planning, and a simple dispatcher UI that explains why a route changed so drivers trust it.
Results
Verified outcome metrics are not available for publication yet.
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