Industry

AI and software engineering for logistics

Logistics software succeeds or fails on how it handles the day going wrong. Plans are easy; the value is in what happens when a vehicle breaks down, a dock is blocked, or a shipment is short. We build planning, visibility, and exception-handling systems designed around the disruption rather than the happy path.

Discuss a logistics project All sectors

What makes it hard here

The plan is a starting position
Inputs are dirtier than the spec admits
Offline is a normal state

Services this involves

Different constraint?Tell us about it

What is different here

Where logistics projects actually go wrong

Margins in logistics are thin enough that small percentage improvements are worth real money, which makes it one of the few sectors where optimisation genuinely pays for itself. It is also a sector where the data is messier than anyone expects — scanned paperwork, inconsistent addresses, timestamps from systems that disagree.

That messiness is where AI earns its place here. Not in replacing the planner, but in cleaning up the inputs the planner depends on and flagging the exceptions early enough to act on.

The plan is a starting position

Systems that only model the intended sequence break on contact with a real operating day. Exceptions are the primary flow, not an error path bolted on at the end.

Inputs are dirtier than the spec admits

Addresses are inconsistent, documents are scanned, and clocks disagree. Ingestion has to normalise and flag rather than assume, or the optimisation runs on numbers nobody trusts.

Offline is a normal state

Drivers and warehouse staff lose signal routinely. Anything used in the field queues locally and reconciles later, or it will be abandoned within a week.

What we build

Systems we build for logistics & supply chain

Route and load planning

Optimisation against the constraints that actually bind — vehicle capacity, delivery windows, driver hours — with the ability to replan mid-day.

Shipment visibility

One reliable view of where things are, assembled from carrier feeds, scans, and telematics that each tell a slightly different story.

Document and proof-of-delivery processing

Bills of lading, customs paperwork, and signed dockets extracted into structured data instead of a folder of images nobody can query.

Exception detection

Flagging the shipment that will miss its window while there is still time to do something, rather than reporting it afterwards.

Related services

The services this usually involves

Questions

Logistics & supply chain: common questions

Can AI reduce logistics costs?

Sometimes substantially, but rarely where people first look. The reliable gains come from cleaning up inputs and catching exceptions early rather than from a smarter routing algorithm — most operations lose more to bad data and late information than to suboptimal routes.

Do you integrate with existing TMS or WMS platforms?

Yes, and it is usually the bulk of the work. What matters is what the incumbent exposes: a documented API is straightforward, a nightly CSV export is workable, and a system with neither means the integration is the project rather than a step in it.

Will field staff actually use it?

Only if it works with poor signal, on the device they already carry, in fewer taps than what it replaces. Field tooling that ignores any of those three gets abandoned regardless of how good the back office thinks it is.

Other sectors

Building something in this sector?

Tell us the constraint you are working against. If we have not solved that particular problem before, we will tell you.

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