Industry · Forwarders, carriers, 3PL, distribution
AI agents for logistics that keep pace with the freight.
Freight forwarders, carriers, contract logistics providers and distribution companies handle thousands of orders, documents and exceptions a day, in many formats and languages, around the clock. We build AI agents that take the reading, retyping and chasing out of that work, connect to your TMS and WMS, and are scoped so the effect shows up within months, not years.
Where AI pays off in logistics
Logistics does not lack automation. It lacks automation for the unstructured parts: the emails, the PDFs, the phone calls and the exceptions.
Your TMS and WMS handle the structured flow well. The expensive work sits around them: a dispatcher retyping a shipment order from an email, a customs clerk comparing a commercial invoice with the packing list, a customer service agent chasing a carrier for an ETA and writing the same update to three customers. This is language work, and language models are good at it.
We build agents that read whatever arrives, in whatever format and language, extract what your system needs, check it against your rules and master data, and either complete the transaction or hand a prepared exception to a person. Wherever a customer, a customs authority or a carrier commitment is involved, a person confirms. Everything else runs.
We work with forwarders, road, sea and air carriers, contract logistics providers and distribution companies in Germany, Austria, Switzerland and across Europe, in English and German. We know that in this industry a business case has to appear in the numbers within months, not years, and we scope accordingly.
- Typical starting pointsOrder intake, customs documents, exceptions, freight audit
- SystemsTMS, WMS, ERP, carrier portals, EDI, email
- Operating principleAgents handle the routine, people handle the commitment
- First stepReadiness assessment, or a 6–8 week pilot
Use cases in freight, contract logistics and distribution
Seven workflows we see across forwarders, carriers and 3PLs, each with the point where a person stays in control.
Shipment order intake from emails and PDFs
Orders arrive as free-text emails, PDF attachments, spreadsheets and portal exports. An agent extracts shipper, consignee, goods, dimensions, dates and references, validates them against master data and creates the shipment in the TMS. Incomplete orders go back to the customer with a precise question, drafted by the agent, sent by the dispatcher.
Customs and export document preparation
The agent assembles commercial invoice, packing list, certificates and transport documents, checks HS codes, values, weights and parties for consistency, flags missing or contradictory data and prepares the declaration draft. A customs specialist reviews and files. Nothing reaches the authority without a person.
Exception handling with customer communication
Delays, damages, missed pickups and address problems trigger the same routine every time: find out what happened, decide the next step, inform the customer. Agents gather the facts from TMS, telematics and carrier messages, propose the action and draft the update. The person on shift approves and sends.
Track-and-trace and customer agents
Customers ask where the shipment is, when it will arrive and what a document means. An agent answers from live TMS and carrier data, in the customer's language, by email, chat or portal, and escalates claims and complaints to a person. Fewer status calls, faster answers. See customer service agents.
Carrier communication, dispatch support and slot booking
Rate requests, confirmations, slot bookings at ramps and terminals and the back-and-forth around them are mostly repetitive text. Agents send the requests, parse the replies, compare offers and prepare the booking. The dispatcher chooses and commits.
Invoice and freight-cost auditing
Carrier and supplier invoices are matched against quoted rates, contracts, surcharge tables and actual shipment data. Deviations are listed with reasoning and the disputed positions are drafted. Accounting approves and pays. Read more under AI agents for finance.
Knowledge assistants and demand signals
A retrieval assistant for drivers, warehouse staff and dispatchers over handling rules, dangerous-goods procedures, customer SOPs and site instructions, in their languages. For planners, agents condense order intake, customer forecasts and market signals into a weekly briefing. See operations.
Constraints specific to logistics
Six realities of this industry that decide whether an agent survives contact with a Monday morning.
TMS and WMS integration
Agents only pay off when they write into the systems that run your operation, not into a separate tool. We integrate with your TMS, WMS and ERP through their APIs and, where an older system has none, through files, EDI messages or a controlled interface layer. The record of the transaction stays in your system.
Time criticality and 24/7 operations
A cutoff missed by ten minutes is a missed vessel. Agents run around the clock, but they need escalation paths to whoever is on shift, hard time limits and a safe fallback when a system is unreachable. We design for the night shift, not for the demo.
Many partners, many formats
EDI messages, portal uploads, emails, scanned PDFs, a photo of a delivery note. Every partner has its own format and the mix changes monthly. Language models handle this variety better than templates, but they need validation rules and confidence thresholds so that a misread digit does not become a misrouted pallet.
Customs compliance
Tariff classification, origin, export controls and sanctions screening carry legal liability. Agents prepare and check; qualified people declare. We build the audit trail so you can show who confirmed what, and we keep the agent out of any step where the law requires a person.
Multilingual operations
Drivers, warehouse teams, partners and customers work in different languages. Agents read and write German, English, Polish, Romanian, Turkish or whatever your network speaks, and knowledge assistants answer in the language of the person asking.
Thin margins, quick payback
There is no budget for a science project. We size pilots so the effect shows in hours saved, error rates and cycle times within the pilot itself, and we prefer model costs that scale with volume over fixed platform fees you carry through a weak quarter.
How we start in a logistics company
Follow the freight
We spend time with dispatch, customer service, customs and accounting, watch how orders and exceptions actually flow, and count the retyping, chasing and checking. This becomes the baseline.
Pick the first process
Usually order intake, exception handling or freight audit: high volume, clear rules, visible pain. We define scope, human checkpoints, the integration path and the target metrics.
Pilot in live operations
We build the agent on your real emails, documents and TMS data, run it alongside the team, tune extraction rules and thresholds with them, and measure against the baseline.
Roll out and keep it running
Go-live with monitoring, alerting and an escalation path for the shift. Then the next lane, the next site or the next process. Managed AI operations covers the running.
Frequently asked questions
Our TMS is old and has no API. Can agents still work with it?
Usually yes. Many logistics systems accept files, EDI messages or database views even without a modern API, and some can be operated through their user interface by a supervised computer-use agent. We examine the integration options in the first two weeks and choose the most robust one. If the only path is fragile, we say so and start with a process where the integration is simple.
How do you stop an agent from misreading an order?
With the same discipline you apply to a new employee: validation against master data, confidence thresholds, plausibility rules for weights, dimensions and dates, and a person who sees everything below the threshold. In the pilot we measure the error rate against manual entry before the agent may create shipments unsupervised, and every record it creates carries the source it came from.
Can agents file customs declarations?
They prepare and check declarations; a qualified person reviews and files. Customs and export-control law places responsibility on people and licensed entities, and we build the process so that it is always clear who confirmed what. The time saving comes from the preparation and the consistency checks, which is where most of the hours go anyway.
How quickly do we see a return?
We scope pilots so the effect is measurable inside the pilot: minutes per order, exceptions closed per shift, invoice deviations caught, response times. Whether that becomes a return depends on your volumes and rates, which is why we build the business case with your numbers before the build starts. We do not promise a figure; we agree the metric and measure it.
Does this work for a mid-sized forwarder without an IT department?
Yes. Mid-sized forwarders are exactly the companies this is designed for. We use platforms and hosted models so your team does not have to operate infrastructure, keep the setup documented and simple, and offer managed AI operations if you want us to keep the agents running. See also how we work with the Mittelstand.
Related
Agentic workflow automation
Multi-step business processes automated end to end by LLM-powered agents across ERP, CRM, ticketing and email, with human approval steps built in.
AI agents for operations & supply chain
Order intake from email and PDF into the ERP, document processing, supplier follow-ups and exception handling, with people deciding on every deviation.
AI agents for customer service
Ticket triage, agent assist with sources, automated resolution for defined categories and a clean hand-over to people when confidence drops.
Choosing your first AI agent use case
A scoring framework, the traits of good and bad first use cases, eight concrete candidates and a pilot design that proves something.
Let's find the first workflow worth automating.
A 30-minute intro call, no slides and no obligation. We listen, ask about your processes, and tell you honestly where AI agents would pay off and where they would not.