Logistics intelligence
AI that understands logistics — and states what it does not know.
Every completed shipment produces data: real lane costs, real carrier performance, real delay patterns. That data becomes the intelligence behind the next shipment.
Capabilities
Where AI adds operational leverage.
Shipment planning
Structure a requirement into an executable plan.
Carrier matching
Match cargo to suitable, documented capacity.
Route recommendations
Corridor and sequencing options for the lane.
Cost estimation
Indicative cost ranges, clearly labelled as estimates.
Load optimisation
Better use of available vehicle capacity.
Return-load opportunities
Compatible cargo on the return leg.
Delay prediction
Patterns that signal schedule risk.
Warehouse selection
Storage options near the route or destination.
Delivery scheduling
Sequenced pickup and delivery windows.
Document assistance
Checklists and completeness checks per shipment.
Logistics reporting
Operational summaries for management review.
Natural-language intake
Describe the shipment in your own words.
The assistant is designed to extract structured fields from plain language, then ask only for what is missing.
“Move 12 tons of bottled water from Addis to Hawassa on Monday.”
- cargobottled waterweight12 tonsoriginAddis AbabadestinationHawassapickupMonday
Missing: vehicle preference and delivery deadline. Shall I use a standard curtain-side trailer and same-day delivery?
Guardrails
What the assistant will never do.
- Never invents prices, availability, carriers, delivery times, regulations or shipment status.
- Unconfirmed values are labelled “estimated / requires confirmation”.
- Missing information is requested rather than assumed.
- Operational confirmation stays with people and partners, not the model.