Logistics Management Bot
Intelligent tracking, routing and exception handling across the logistics chain.
The brief
Shipment status lived in fragments — across carriers, warehouses and spreadsheets — so problems surfaced as complaints rather than alerts, and routing decisions were made on stale information.
The client wanted one intelligent layer that tracked movement in real time, optimised routing, and surfaced exceptions early enough to act on.
- Consolidate fragmented shipment status into one live view.
- Optimise routing and resource allocation.
- Predict ETAs that update with real conditions, not static schedules.
- Flag delays and anomalies proactively, before they become complaints.
- Automate routine coordination — updates, reassignments, notifications.
- Give operations a single dashboard for oversight and control.
Our AI-native approach
We built a logistics platform that consolidates tracking into one live view, applies routing optimisation, and runs an exception engine that flags delays and anomalies proactively. Routine coordination — updates, reassignments, notifications — is automated.
Following our AI-driven agenda, models for ETA prediction and exception detection were trained on the client's own movement history.
What we built
Real-time tracking
One live view of shipments across the chain.
Routing optimisation
Smarter allocation of routes and resources.
ETA prediction
Data-driven arrival estimates that update with conditions.
Exception engine
Delays and anomalies are flagged proactively.
Automated coordination
Updates, reassignments and alerts run automatically.
Operations dashboard
A React dashboard for live oversight and control.
How we built it
We trained ETA and exception models on the client's own movement history so estimates reflected real conditions, and we consolidated tracking into one event-driven view before layering any intelligence on top.
A managed pod delivered in phases — tracking first, then prediction, then optimisation and automation — measuring each against live operations. Automation handles the routine; genuine exceptions are surfaced for people.
How it works
Track
Movement data is consolidated in real time.
Predict
ETA and risk models score each shipment.
Optimise
Routing and allocation are improved continuously.
Flag
Exceptions are surfaced before they escalate.
Act
Routine coordination is automated; people handle the rest.
ETA prediction and exception detection are trained on the client's historical movement data, so estimates reflect real-world conditions rather than static schedules. Routing optimisation balances cost, time and constraints rather than chasing a single metric.
Automation handles the routine while surfacing the genuine exceptions for human attention.
The impact
Status became a live, single view instead of scattered fragments.
Problems surfaced as early alerts, not customer complaints.
Routing decisions used current data, improving cost and time.
Routine coordination ran without manual effort.
Technology stack
Building something similar?
If this maps to a problem you're facing, tell us what you're building. We'll show you how we'd engineer it — and come back within one business day.