Logistics software that keeps up with what happens on the road.
Shipments move through carriers, warehouses, drivers and customs brokers, and the data about them arrives as emails, PDFs, scans and phone calls. We help logistics, supply chain and field operations businesses connect those pieces, automate the paperwork with AI, and build tools that work for dispatchers and drivers. Our AI-native engineers handle the integration grind quickly while senior people stay accountable for reliability.
Follow one shipment and count the handoffs
Most logistics technology problems are not dramatic failures. They are small gaps at each handoff that add up to late updates, disputed invoices and staff re-keying data all day.
Booking arrives
OrderSome customers send EDI or API bookings; many still email spreadsheets and PDFs that someone types into the TMS. AI extraction can take over much of that typing.
Warehouse handling
WMSReceiving, putaway, picking and packing rely on scans. Missed scans and manual overrides create stock and status records nobody fully trusts.
Dispatch and routing
PlanPlanners balance time windows, vehicle capacity, driver hours and last-minute changes, often in their heads or a spreadsheet.
Last mile and proof of delivery
DriverDrivers need an app that works with patchy signal, captures photos and signatures, and does not add steps at every stop.
Exceptions and billing
CloseFailed deliveries, accessorial charges and carrier invoices must be reconciled against what actually happened, which is where margin quietly leaks.
AI in logistics that works on a Monday morning
The best AI use cases in logistics take on the repetitive reading, sorting and chasing that operations teams do every day, not grand autonomous supply chains.
Shipping document automation
Extract data from bills of lading, commercial invoices, packing lists and delivery orders, with confidence scores and human review for low-certainty fields.
Email and booking parsing
Turn customer and carrier emails into structured bookings, updates and quote requests inside your TMS instead of an inbox.
Predicted arrival times
Improve ETAs using historical transit times, stop durations and live location, so customer updates reflect reality.
Exception triage
Flag shipments likely to miss their window and draft the customer message, so the team acts before the complaint arrives.
Proof of delivery checks
Review delivery photos and signatures for missing or unclear evidence, reducing disputes weeks later.
Freight invoice audit
Match carrier invoices against agreed rates and delivery records, and highlight overcharges for a person to query.
Manual planning, off-the-shelf routing or custom route optimisation?
Route optimisation is a well-studied problem (the vehicle routing problem), and good software exists. The right choice depends on how unusual your constraints are.
| Dispatcher experience | Off-the-shelf routing software | Custom optimisation | |
|---|---|---|---|
| Setup time | None | Days to weeks | Weeks to months |
| Handles standard time windows and capacity | Up to a point | Yes | Yes |
| Unusual constraints | Handled by judgement | Limited to built-in options | Modelled explicitly |
| Scales with volume | Poorly | Well | Well |
| Integration with your systems | Manual | Via connectors or APIs | Designed around them |
| Best for | Small, stable fleets | Most delivery operations | Complex or specialised networks |
Custom optimisation usually builds on established open-source or commercial solvers rather than starting from scratch. Keep your dispatchers involved: their knowledge becomes the constraints.
Why logistics technology projects stall, and how we would avoid it
Logistics software lives in a messy environment. Carriers and partners each have their own formats, some modern APIs and some decades-old EDI. Transport and warehouse management systems are often heavily customised. Drivers and warehouse staff have seconds, not minutes, to interact with a screen. Projects stall when they are designed in an office and meet that reality late.
We would start by shadowing the actual workflow: a dispatcher planning tomorrow, a driver completing a route, an administrator processing a stack of shipping documents. From there, the priority is usually integration and data quality before anything clever, because an AI model reading a bill of lading is only useful if the extracted data lands in the right system with the right shipment reference.
Integration-heavy work is also where AI-native delivery shines. Our engineers use AI coding agents to generate and test adapters for carrier APIs, EDI message mappings and document parsers far faster than writing each by hand, while a senior engineer designs the data model, offline sync and failure handling. Field software gets tested on real devices in poor signal conditions, not just on a laptop.
Signs your operations have outgrown their systems
Customers call to ask where their shipment is because your portal is out of date
Staff spend hours a day re-typing data from emails and PDFs
Route plans live in one planner's head or spreadsheet
Proof of delivery disputes take days to resolve
Carrier invoices are paid without checking against agreed rates
Adding a new customer or carrier requires a developer each time
Questions we often hear
How is AI used in logistics and supply chain management?
Common uses include extracting data from shipping documents, parsing booking and status emails, predicting arrival times, forecasting demand, flagging shipments at risk of delay and auditing freight invoices. The most reliable results come from applying AI to specific, repetitive tasks with human review for exceptions, rather than trying to automate whole operations at once.
Can AI accurately read bills of lading and customs documents?
Modern document AI handles varied layouts, scans and even handwriting far better than older template-based OCR. Accuracy still varies with document quality, so production systems should score confidence per field, send uncertain values to a person, and validate against known data such as shipment references and HS code formats before anything is filed.
Should we build a custom TMS or buy one?
Most companies should buy a transport management system and customise integrations around it. Building a custom TMS makes sense only when your operating model is genuinely different, such as specialised freight, unusual pricing or a platform business model. A common middle path is buying the core and building custom portals, driver apps or automation on top.
Do driver apps need to work offline?
Yes, for nearly every field operation. Drivers lose signal in basements, loading bays and rural areas, so the app should store stops, photos and signatures locally and sync reliably when connectivity returns, without creating duplicate or lost records. Designing this properly from the start is far cheaper than retrofitting it.
How long does it take to build a shipment tracking portal?
A customer tracking portal on top of an existing TMS with usable APIs can often be built in a matter of weeks by an AI-native team. Timelines grow when tracking data must be assembled from several carriers, telematics providers or legacy systems, which is where most of the effort usually goes.
Related reading and tools
Tell us where your operations lose time.
Paperwork, routing, tracking updates or a system that no longer fits: describe the workflow and we will reply within 24 hours with an honest view on what to fix first and where AI helps.
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