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An AI assistant is planned. Until it is genuinely useful we would rather point you at the page that actually answers your question.
Logistics software runs continuously and is used by people in vehicles, warehouses and depots rather than at desks. Deployment windows are narrow, downtime is costly, and the interface has to work for a driver holding a phone in one hand.
Location data is high-volume and irregular. Vehicles report frequently, lose signal in tunnels and rural stretches, and send batches on reconnection. Systems must handle gaps and out-of-order arrival without producing nonsensical routes or false alerts.
The domain has genuine algorithmic depth. Route optimisation with time windows, capacity limits and driver hours is computationally hard, and practical systems use heuristics that produce good-enough answers quickly rather than optimal ones slowly.
Constraints
These are the factors that change architecture rather than decorate it.
Deployments and migrations without downtime, since operations run through nights and weekends.
Driver apps that function offline and reconcile cleanly, with device timestamps preserved.
Driver hours, vehicle checks and consignment documentation retained in auditable form.
Signatures, photographs and timestamps captured reliably and linked to the consignment.
Applications
Live location, status and telemetry with historical replay for investigating incidents.
Multi-stop routing respecting time windows, vehicle capacity and driver hour limits.
Job lists, navigation handoff and proof of delivery, fully functional without a connection.
Receiving, picking and dispatch with scanning, integrated with inventory and order systems.
Integrations
Common integration points in this sector. Others are handled case by case.
FAQ
Tell us what you are trying to build or fix. We will come back with scope, approach and an honest view on cost and timeline.