Logistics.

Logistics operators do not need generative AI in the routing engine. They need it in the exception lane, where a damaged document, mismatched HS code, or unhappy consignee costs more than the freight itself. In practice, the first deployments cluster around structured extraction, customer-care deflection, document intelligence, agentic workflows: areas where evaluation criteria are objective, data is already in the system, and an evaluation harness can measure quality continuously.

Regulators-of-note for logistics engagements typically include ACCC, Customs / border-agency rules, industry-association codes. The specific regulators that bind a given engagement depend on jurisdiction and the data classes in scope, so we map this explicitly during discovery rather than assume a global posture.

We do not deploy agents that act on physical-fulfilment systems autonomously. Agentic workflows propose actions for human dispatchers to approve. This is a deliberate trust boundary, not a capability gap. We are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in this industry does not justify it.

Our logistics engagements typically sit between Chief Operating Officer, Head of Operations, Director of Customer Service and equivalent senior operators. The decision-maker varies by organisation, but the common thread is a leader accountable for both delivery and downside.

In logistics, generative AI tends to attach to exception handling, shipment-document extraction, and customer-service triage rather than core routing. The same techniques look superficially similar across industries, but the data shapes, evaluation criteria, and adverse-event posture differ enough that copy-pasting an engagement from another industry usually produces a system that fails the first audit it sees.

We spend two weeks with your leadership and operators, rank the use cases that are worth doing, and come back with a clear plan and a clear price. We never start with a six-month strategy exercise. We go after the smallest piece that produces real value first.