AppMatic Tech engineers AI route-planning agents that build and adjust delivery routes against live orders, vehicle constraints, and traffic, built with Node.js and LLM orchestration over governed logistics tools and an optimisation engine. Dispatchers approve; the agent handles the reshuffle. 330+ products shipped since 2017.
Route plans break the moment an order changes or traffic shifts, and manual re-planning is slow. AppMatic Tech builds an agent that proposes and adjusts routes against live constraints, leaving the dispatcher to approve.
AI Agent Development Stack for Logistics
| Layer | What AppMatic Tech uses |
|---|---|
| Agent runtime | Node.js orchestration, tool-calling LLM |
| Optimisation | Route optimisation engine under the agent |
| Data access | Orders, vehicle, and traffic tools (often via MCP) |
| Workflow | Proposed plans with dispatcher approval |
| Audit | Plan and change logging |
How AppMatic Tech Delivers This
- Connect the agent to orders, vehicle constraints, and traffic data.
- Generate route plans via an optimisation engine the agent calls.
- Adjust plans as orders and conditions change.
- Route proposals to dispatchers for approval.
- Validate against operations and deliver with a support SLA.
Proof: Related AppMatic Tech Build
A transport platform AppMatic Tech built to coordinate movement and status data across a fleet.
Frequently Asked Questions
- Does the agent replace the dispatcher?
- No. AppMatic Tech builds the agent to propose and adjust routes against live constraints, with dispatchers approving the plan, so it removes manual re-planning while keeping human control.
- How does the agent optimise routes?
- AppMatic Tech pairs the agent with a route-optimisation engine it calls as a tool, so plans respect vehicle constraints, time windows, and traffic rather than relying on the model alone.
- Can it react to changes during the day?
- Yes. AppMatic Tech connects the agent to live order and traffic data, so it re-plans when a new order arrives or conditions change and surfaces the adjustment for approval.
Further reading: AI Agent Development: Architecture Patterns from 12 Deployments