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Research report
AI Does Not Remove the Bottleneck
Find your real workflow constraints before buying AI tools.
Fleet, freight and warehouse platforms, plus the data work that makes their numbers agree.
The work we are asked to do in logistics.
Tools that work on a cracked handset with two bars of signal.
Connecting systems that each believe they are the authoritative one.
One operational view that dispatch, drivers and customers can all be shown.
Planning software built on how your operation dispatches.
The status page that stops the phone ringing.
The numbers behind our logistics work
Every logistics business we meet is rich in data and poor in agreement. The tracking layer is usually fine; what is broken is that three systems each hold a different answer for where an order is, and none of them is authoritative. That is a governance decision before it is an engineering one. Settle the ownership question and the dashboard almost builds itself.
the scale we have run
what it moved
years in operational software
Engagements in logistics, described rather than named.
That is the most common brief we get in this sector, and it is a data-ownership problem before it is a technology one. Three systems each hold a defensible answer because each was told something different. We establish which is authoritative for each fact, then make the others follow it.
Usually, and the method depends on what the vendor exposes. Modern platforms have APIs; older ones may offer a database view or a scheduled file, and both can be worked with honestly. What we avoid is automation driving a screen, because that breaks silently the first time the vendor changes a layout.
They have to. Drivers and warehouse staff work where the network does not, so offline is an architectural decision taken at the start — local storage, a sync strategy, conflict handling when two people change the same record, and telling the user plainly what has and has not saved.
Real-time visibility is a data-agreement problem wearing a dashboard. The tracking layer is usually fine; what is broken is that the systems feeding it disagree. We fix the agreement first, because a live dashboard over inconsistent data just shows you the disagreement faster.
Proof of delivery, customs documents and compliance records are where a lot of the manual time actually goes, and they are well suited to extraction and automation. The exceptions still need people, so the design makes the exception route obvious rather than hiding it.
Yes, and each carrier is its own integration with its own quirks. The pattern that survives is a normalised internal model with a thin adapter per carrier, so adding the next one takes days. Building directly against one carrier's format is the decision that costs later.
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Research report
Find your real workflow constraints before buying AI tools.
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Research report
Don't run your tech stack on unverified SaaS myths.
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Research report
Close the gap between buying AI and adopting it.