From route optimisation and dynamic ETAs to predictive maintenance, document AI and computer vision on the loading dock, AI is changing how transport companies, fleet owners and logistics planners run their operations. We help organisations make the move from isolated pilots to AI that genuinely lives in day-to-day planning.
The term AI in transport covers a broad field: from classical optimisation algorithms (route and planning models based on OR-Tools and MILP solvers) to modern machine learning for ETA prediction, computer vision for damage inspection, and large language models that read freight documents. In practice, transport companies use a combination; no single technique covers all the problems.
We see the same patterns with couriers and last-mile operators, with 3PL and 4PL planners, with fleet owners running lorries, with multimodal shippers combining rail, waterway and road, and with MaaS and car-sharing platforms. The problem is rarely 'we have no data'. It is usually 'we have ten systems with disconnected data and no one bringing it together'. That is what we build: integrations between TMS, telematics, ERP and planning systems, with an AI layer on top that supports operational decisions rather than taking them over.
It is important to make clear from the outset that AI in transport is not a self-driving truck. Autonomous vehicles are a separate field with their own regulation and R&D horizon. What we are talking about is the AI layer on your existing operation: drivers, planners and dispatchers stay in control, and AI handles the work people cannot do quickly or consistently enough.
The planner works with Excel, a map and years of experience. That works, but it is fragile: one planner off sick and the operation stalls. AI-supported route optimisation gives you a reliable baseline from which people can steer.
Static ETAs from the TMS ignore current traffic, weather conditions and the history of a specific route. Dynamic ETAs significantly reduce the number of 'where is my shipment' phone calls.
The data from the FMS bus and telematics is already held in Webfleet, Trimble or Fleetboard. With predictive maintenance, patterns become visible before a fault occurs, and you schedule maintenance at a convenient time rather than at the roadside.
Document AI reads consignment notes, CMRs, customs forms and tax documents with high accuracy. A planner or administrative employee still checks, but no longer has to type.
Time windows, parcel lockers, home delivery, low-emission zones, load mix: the last-mile puzzle has too many variables for a human planner. AI routing with live time-window optimisation reduces the number of failed deliveries.
You will soon need to be able to state the CO2 footprint of each shipment. Carbon-tracking AI calculates this based on route, vehicle and load, and at the same time shows where reductions are achievable.
You already work with Transwide, Descartes, Manhattan, Trimble Transportation or Centric. We build an AI layer on top that combines data from the TMS with telematics and external sources, without forcing your planning team onto a new system.
Sometimes the optimisation logic you need doesn't fit a standard package such as Loginext, FarEye, Onfleet or Routific. In that case, we build a targeted custom component that connects seamlessly to what you already run.
Multimodal flows, complex yard management at a large distribution centre, or a matching platform for empty return trucks: for specialised flows, we build a platform from the ground up, using OR-Tools, proprietary ML models and integrations at CAN bus level.
An overview of where AI delivers value in practice, based on conversations with planners, fleet owners and logistics directors. Not every application makes sense for every organisation; often one well-chosen quick win is more valuable than five pilots running at once.
Multi-stop, multi-constraint: time windows, collective labour agreements, fuel consumption and driver preferences, all in one model.
Arrival times that adjust in real time for traffic, weather and historical patterns on that specific corridor.
Which route, which vehicle type and what volume you can expect in the next hour, day or week.
Which lorry goes to which dock, with insight into timely availability and turnaround time on the site.
Engine and sensor data from the FMS bus and telematics predict faults well before they occur.
Damage inspection at pickup, number plate recognition at access points, dock cameras for loading and unloading times.
Automatically read, validate and pass on CMR consignment notes, customs papers and invoices to your TMS or ERP.
Urban routes with locker box allocation and customer time-window optimisation.
Handle track-and-trace, delivery updates and simple claims without burdening the service desk.
Plan in line with collective labour agreements, monitor rest periods and respect driver preferences.
Calculate CO2 emissions per shipment for CSRD reporting and internal reduction targets.
Matching empty return trucks to demand, in the spirit of Quicargo and sennder.
Good AI in transport starts with the right data at the right level. On the vehicle itself we work with OBD-II, FMS bus and CAN bus; in the cab with telematics platforms such as Webfleet, Trimble and Fleetboard. At infrastructure level, a streaming layer (Kafka or Pulsar) combines engine data, GPS, cargo events and order streams into a single source for models.
For routing and planning problems we often turn to classic optimisation solvers: OR-Tools, MILP solvers and, depending on scale, specially trained reinforcement learning models. For ETA and demand forecasting we use gradient boosting and time-series models. For computer vision at the dock, damage photos or number plates we work with YOLO variants and modern vision APIs. For document AI we use LLM extraction, integrated as described on our custom LLM integrations page.
We solve routing and geo questions with the right mix: OpenStreetMap, HERE, Google Maps and TomTom each have their strengths. For dense urban last mile, HERE or TomTom is often more accurate than Google Maps; for open-source routing with your own restrictions, OSRM based on OpenStreetMap is the right choice. We choose per use case and isolate that choice in a service layer, so a switch later doesn't require a redesign.
On the off-the-shelf software side, on the TMS side we encounter Transwide, Descartes, Manhattan, Trimble Transportation and Centric; on the WMS side Manhattan, Blue Yonder and Centric. AI vendors such as Loginext, FarEye, Onfleet, Routific and Optidrive cover specific sub-problems. We are vendor-agnostic: where an off-the-shelf product works well, we use it. Only where standard packages reach their limits do we build custom solutions.
Location data about people is personal data. Under the GDPR there must be a legal basis (collective labour agreement arrangements, works council consent) and data minimisation. We build privacy by design: raw data is kept briefly, aggregated patterns for longer.
The EU AI Act classifies AI systems that monitor employees as high-risk. That means extensive documentation, human oversight and transparency. We integrate these requirements from sprint one rather than as a compliance stamp afterwards.
A rostering model that ignores driving and rest times or the CAO is immediately unusable. We model these rules as hard constraints, not 'preferences'.
The digital consignment note (e-CMR, in line with DCD) is gaining ground internationally. Document AI flows you build now will need to connect seamlessly to it, and we design with that in mind.
For most transport clients, CSRD reporting is coming sooner than expected. A carbon-tracking layer in your AI stack is soon not a luxury but a requirement.
Transport falls within the sectors covered by NIS2. Securing the AI layer, including access control, logging and audit trails, becomes part of your wider security policy.
From our experience, a good start in AI for transport is not a big-bang roadmap but one carefully chosen use case where the impact is measurable. That is usually either an ETA prediction on a single corridor, a document AI flow on CMRs, or predictive maintenance on one vehicle type. Something that goes live in a few sprints and that the planning team can use straight away.
We build outwards from that first success. Only once the first flow runs reliably and has earned the trust of planners and drivers do we expand to the next domain. That order is not organisational caution; it is practical. AI systems that are not useful to the people on the shop floor from day one are rarely adopted, however good the model.
We often work alongside your existing TMS vendor, telematics partner and, where relevant, your fleet management team. The pages on building transport software and developing a fleet management app explain more about what our broader transport practice looks like. If you'd rather explore the visual side of AI (damage inspection, number plate recognition, dock cameras), see computer vision applications.
For organisations that want to think beyond a single use case, covering multiple domains, a genuine AI roadmap, governance and staff training, we work within our wider practice on enterprise AI implementation. There, the greater the ambition, the more important the right sequence. We help with that order, not just with the code.
Our AI transport practice serves a broad range of organisations: transport companies with their own fleets, couriers and last-mile operators, 3PL and 4PL planners, fleet owners, multimodal shippers (rail, waterway, road), distribution centres linking logistics and warehousing, and MaaS or car-sharing platforms that want to work with data. Public transport organisations (NS, GVB, RET) and autonomous vehicle start-ups form more of the market context in which we operate than our direct client base, although we regularly share insights from other projects with them.
A half-hour introductory conversation in which we review where your data currently sits, which use case holds the most promise, and what a first sprint would look like. No obligation, in your language, and without a demo of an AI platform you don't really want.
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