Case studyIn progress

Building a calmer workflow for container logistics.

An illustrative account of a transport management system and AI agent team being built for a container-logistics operator at Laem Chabang.

AI-generated illustration

The challenge

Critical work moves through manual hand-offs.

Delivery information, proof-of-delivery photos and invoice details need to stay aligned across a busy container operation. Each hand-off creates another chance for missing information, repeated checking and delayed follow-up.

The goal is not to remove people from operational decisions. It is to remove the routine coordination around those decisions.

The approach

One operational system, with agents around the repetitive work.

We are building a transport management system as the shared operating layer. A team of specialised AI agents handles defined tasks around that system, while a person reviews exceptions and important decisions.

Operational inputsAgent teamHuman reviewTMS record

What we are building

Delivery-note entry

Read incoming delivery notes, structure the relevant fields and route uncertain details for confirmation before posting.

Proof-of-delivery collection

Track required photos, follow up on missing evidence and connect approved files to the right job.

Invoice reconciliation

Compare records across the workflow, flag mismatches and prepare a clear review queue.

Status and design principles

The system is in progress.

The workflow is currently being built and will require validation in representative operational scenarios. No performance claims are being made at this stage.

Design the human review point before automating the routine path.

Use operational exceptions to refine the process model.

Treat the shared system and its agents as one operating design.

Human review is built into every agent workflow. Important calls and anything outside agreed rules go to a person.

Start with one workflow

What would your team stop doing tomorrow?

Show us the repetitive task. We will map the workflow, find the practical AI opportunity, and tell you what is worth building.

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