AI route and territory optimisation
We cut field-force travel and fit in more visits a day. By combining operations research with machine learning, we plan smarter territories and routes for reps and agents who visit hundreds of locations - in healthcare, distribution and sales.
When reps or agents visit hundreds of partner locations, the day is won or lost on planning. Manual territories and routes leave people stuck in traffic, double back across zones, and run out of day before they run out of visits. The cost is real - in fuel, in missed calls, and in the visits that never happen.
Density-based clustering (OPTICS) groups locations into balanced territories that respect real geography - dense cities and sparse rural runs handled differently, outliers flagged rather than averaged away.
We model the road and transit network as a time-dependent graph, so a route is planned against how long each leg actually takes at that time of day, not a flat distance.
Visit order is solved as a vehicle-routing problem with time windows, using shortest-path search (Dijkstra and A*) and constraint programming (CP-SAT) to fit the most visits into a working day.
Models predict travel time from traffic, weather and time of day, and the likelihood of a no-show or cancellation - so the plan books a realistic day, not an optimistic one.
Routing is treated as sequential decision-making: the system re-routes in real time as conditions change, and learns from outcomes to plan better next time.
Drive plus public transit through transfer nodes is chosen only when it genuinely cuts total trip time - the engine optimises the journey, not just the drive.
Across our route-optimisation work, teams have seen gains in this range. The exact numbers depend on the territory and the starting point.
A scalable server-to-server API plans the next day's routes for the whole field force overnight, ready before anyone starts.
An embedded SDK gives reps on-device, offline-capable re-routing with in-app turn-by-turn, so the plan survives contact with the real world.
It integrates with your CRM and GPS data and runs on your own mapping subscription (Google Maps Platform, Mapbox or a regional provider) - your billing, your data.
Medical representatives covering a national network of clinics and pharmacies - more meaningful visits, less time in the car.
Field agents and last-mile operations across a wide distribution network, with route and territory planning that scales.
Any team whose people visit many locations on a schedule - smarter territories and routes turn windshield time into selling time.
These case studies are the same route-optimisation engine applied to different field operations - read whichever is closest to yours.
What is AI route optimisation?
It plans smarter territories and routes for field teams that visit hundreds of locations, combining operations research with machine learning to cut travel time and fit in more visits a day - used in healthcare, distribution and sales.
How does WislaCode's route optimisation work?
Density-based clustering (OPTICS) designs balanced territories; the road and transit network is modelled as a time-dependent graph; and visit order is solved as a vehicle-routing problem with time windows, using shortest-path search (Dijkstra and A*) and constraint programming (CP-SAT).
How is machine learning used in the routing?
Models predict travel time from traffic, weather and time of day, and the likelihood of a no-show or cancellation, so the plan books a realistic day. Reinforcement learning re-routes in real time as conditions change and learns from outcomes to plan better next time.
What results can we expect?
Across WislaCode's route-optimisation work, teams have seen around 30% less travel time, about 20% more visits per day, 15-20% lower transport cost and roughly 50% less between-zone travel. The exact numbers depend on the territory and the starting point.
Does it account for public transport, not just driving?
Yes, where it helps: drive plus public transit through transfer nodes is chosen only when it genuinely cuts total trip time - the engine optimises the whole journey, not just the drive.
How is route optimisation delivered?
As a server-to-server API that plans the whole field force's next-day routes overnight, and an embedded SDK that gives reps on-device, offline-capable re-routing in the field. It integrates with your CRM and GPS data and runs on your own mapping subscription.
Tell us how your reps or agents are routed today, and we will show you where an optimisation layer would pay back fastest.