Logistics
AI for routing, prediction, and warehouse intelligence.
Logistics margins are thin and customer expectations for speed and visibility are rising. The operations teams that stay competitive are those that have turned their data into a real-time operational advantage.
Common challenges
The problems logistics teams bring to us most often.
Suboptimal routing decisions
Static routing rules cannot account for real-time traffic, weather, vehicle capacity, and time-window constraints simultaneously at scale.
Delivery time unpredictability
Customers expect accurate ETAs. Most logistics platforms offer only broad windows because they lack the predictive models to do better.
Warehouse inefficiency
Slotting, pick path planning, and labour scheduling in large warehouses are still heavily manual — leaving significant throughput gains on the table.
What we build
Specific systems and capabilities we deliver for logistics clients.
Route optimisation
Constraint-aware routing models that account for traffic, capacity, time windows, and driver hours — reducing cost per delivery.
Delivery time prediction
ML models that produce accurate, narrow ETAs using historical delivery data, real-time conditions, and carrier performance signals.
Warehouse automation
Slotting optimisation, pick path planning, and labour forecasting systems that increase warehouse throughput without additional headcount.
Demand sensing
Short-horizon demand models that feed replenishment and transport planning with signals beyond historical averages.
Anomaly detection
Systems that flag shipment delays, carrier performance outliers, and inventory discrepancies in real time.
Related industries
Other sectors we work with.
Working in logistics?
Tell us what you are building. We will come back with a clear, honest plan — no pitch, no vague estimates.