Agentic AI for logistics

The intelligent layer for the logistics you already run.

Land connects AI agents to your TMS, WMS, ERP and carrier portals. Exceptions, documents and customer queries arrive resolved or ready for a decision. No system replacement.

Propose a pilot How it works
agent · exceptionsrunning
Shipment 48213: ETA +3 h 40 minTraffic hold on the A-2 near Zaragoza
  1. TMSDestination Mollet. Delivery committed for 16:00.
  2. WMSDock 7 booked. Dock 3 free from 19:00 to 21:00.
  3. ContractPenalty if it arrives after 20:00.
  4. EmailCustomer accepts deliveries until 21:00 (thread of 14 Oct).
Proposal · awaiting your approvalMove to dock 3 at 19:45 and notify the customer. Email draft ready.

Illustrative example

Nothing to migrateWe work on top of your systems.
Starts read-onlyYour operation stays untouched.
You set the limitsWhat it proposes and what it executes.
6 to 8 weeksFrom pilot to a data-backed decision.

The problem

Your systems work. The work between them is still manual.

A single delay touches the TMS, the WMS, the customer's email and the carrier contract. No system sees the whole picture, so someone rebuilds it, one tab at a time.

Copying and cross-checking

Delivery notes, CMRs and invoices that someone reads and retypes into another system.

Exceptions handled too late

The delay is known, but nobody matches it in time against the dock, the customer and the penalty.

Knowledge held by a few

Which carrier fails on which lane, and what was done last time, lives in inboxes and in people's memory.

The agentic layer

A new layer on top. Your systems stay in charge.

Agents use the interfaces your systems already expose. Your TMS and WMS remain the source of truth, and results go back to the tools your team already works in.

Your team

Where they already work
Control towerTeamsEmailCustomer portalVoice

Land agentic layer

What we build
Process agentsCited knowledge (RAG)Tools and APIsCase memoryPermissions and audit

Your systems

What already works
TMSWMSERPYMSTelematics and GPSEDICarrier portalsDocuments

A chatbot

  • Answers from what it memorised
  • Knows nothing about your shipments
  • Cannot tell when it is wrong
  • Only talks

An agent on top of your systems

  • Queries your real data and documents
  • Chooses the source it needs at each step
  • Cites where every answer comes from
  • Acts with permissions and within limits

Use cases

Where agents take action.

We pick processes with high volume, known rules and a clear cost when they go wrong.

01

Exception control tower

Detects delays and incidents, works out the impact on docks and customers, and prepares the reschedule and the notice.

TMS · GPS · WMS · contracts
02

Transport documents

Reads delivery notes, CMRs and proofs of delivery, validates them against the order and opens an incident when they do not match.

Email · OCR · ERP
03

Carrier invoices

Checks each invoice against the rate, the surcharges and what was actually carried. Anything that does not match arrives with the difference explained.

ERP · TMS · rates
04

Customer service

Answers “where is my order?” with real data and hands over to a person whatever it cannot prove.

TMS · email · portal · Teams
05

Appointments and docks

Manages time slots with carriers, reorders appointments when there are delays and alerts the warehouse.

YMS · WMS · email
06

Customs and compliance

Reviews foreign trade and dangerous goods paperwork against your procedures, citing each requirement.

ERP · regulations · procedures

Agent-driven analysis

Why have late deliveries increased on the Valencia lane this week?

  1. Turns the question into queriesAcross the TMS, the WMS and the incident records.
  2. Runs and cross-referencesDeliveries by time slot, carrier, dock appointments and incidents.
  3. Finds the patternCompares with previous weeks and rules out causes with no evidence.
  4. Explains and proposesWrites up the cause in operational language, with the data cited.

Illustrative data. The agent generates these views from its queries.

Integration

The data already exists. It just needs connecting, with context.

SourceWhat it gives the agentHow it connects
TMSShipments, routes, ETAs, costs and incidentsREST, SQL, webhooks
WMS and YMSStock, picking, docks and appointmentsREST, SQL
ERPOrders, customers, rates and billingOData, BAPI, REST
Telematics and GPSPosition, driving times and temperatureAPI, MQTT, webhooks
EDITransport orders, dispatch advices and status updatesEDIFACT, XML, SFTP
Carrier portalsTracking, rates and proofs of deliveryAPI or web automation
Email and documentsCMRs, delivery notes, invoices and customer agreementsMicrosoft 365, IMAP, OCR
Incidents and knowledgeHow it was solved last time, proceduresJira, Zendesk, ServiceNow, SharePoint

Governance

Autonomy is earned, one process at a time.

Each workflow has its own level. It only moves up when measurements show the agent does the job well, and it can always move back.

Level 1

Query

Reads and answers with sources. Changes nothing.

Level 2

Propose

Prepares the action. A person carries it out.

Level 3

Execute with approval

Acts once someone approves with one click.

Level 4

Execute within limits

Acts only on reversible cases, with agreed amounts and rules.

Your data stays home

On-premises, in your cloud or managed. The model can run locally too.

Every answer cites its source

Shipment, document, query or incident. No evidence, no answer.

Inherited permissions

The agent sees what the user sees, and every action is logged.

Swappable model

Open or commercial, depending on cost, latency and confidentiality.

How to start

A focused pilot, in 6 to 8 weeks.

  1. Choose the workflow

    One operation and two use cases with a measurable goal.

  2. Connect read-only

    Systems and documents, with no writes anywhere.

  3. Validate with your team

    Dispatch, warehouse and customer service check the results.

  4. Measure and decide

    With pilot data: expand, adjust or stop.

The first deliverable is not a model.

It is a one-page spec per process: what to optimise, with what data, and what a mistake costs. Without it, no agent deserves permissions.

Goal
Cut handling time per incident
KPI
Minutes per incident, OTIF, penalties
Data
ETA, dock appointments, contract, history
Action
Reschedule the appointment and notify the customer
Error
Medium cost, reversible: starts with approval

The team

The people who build it.

Carlos Carné

Carlos Carné

CEO & Computer Vision Lead

Telecommunications engineer. 10 years in industrial perception systems.

Nestor Casado

Nestor Casado

CTO & RAG Systems Architect

LangChain and LlamaIndex contributor. Specialist in retrieval systems at scale.

Miquel Ferrer

Miquel Ferrer

Head of MLOps

ML infrastructure engineer at Amazon Science for 5 years. Expert in distributed serving and inference optimisation.

Maria Flores

Maria Flores

Research Lead

Postdoc at ETH Zurich in multimodal learning. Co-author of 14 papers at CVPR, ICCV and NeurIPS.

Let's talk

Which logistics process would you like to stop handling by hand?

First technical session free, 45 minutes. We look at your case and tell you whether we can add real value.

Hours

Monday to Friday, 9:00 to 18:30 CET

Office

C/ Aragón, 186, 08011 Barcelona

Company

Land Networking S.L. · VAT ID B63106900

Legal information · Land Networking S.L.

This is a courtesy translation. In the event of any discrepancy, the Spanish version prevails.

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In compliance with the duty of information set out in article 10 of Spanish Law 34/2002 of 11 July on Information Society Services and Electronic Commerce (LSSICE), the identifying details of the owner of this website are as follows:

Company name: LAND NETWORKING S.L.
Tax ID (CIF): B63106900
Registered office: C/ Aragón, 186, postal code 08011, Barcelona
Commercial Registry: Registered in the Commercial Registry of Barcelona, Folio 23235, Volume 2, Entry 43
Contact email: gestio@land.es
Phone: +34 902404017

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This Legal Notice governs access to and use of the Website, as well as the information and specialised consultancy services in artificial intelligence, computer vision and retrieval-augmented generation (RAG) systems offered by LAND NETWORKING S.L.

LAND NETWORKING S.L. does not guarantee that access to the Website or its contents will be free from interruptions or errors, or that it will be up to date, although it will use its best efforts to avoid, correct or update them as appropriate.

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In compliance with Regulation (EU) 2016/679 of the European Parliament and of the Council (GDPR), Spanish Organic Law 3/2018 of 5 December on Personal Data Protection and the guarantee of digital rights (LOPDGDD), and Law 34/2002 on Information Society Services, LAND NETWORKING S.L. informs users of the following:

Data controller: LAND NETWORKING S.L., with Tax ID B63106900 and registered office at C/ Aragón, 186, 08011 Barcelona.

Purposes of processing: Personal data collected through the Website's forms will be processed for the following purposes: (i) responding to enquiries and requests for information from the user; (ii) managing the commercial relationship and providing the contracted services; (iii) sending commercial communications about the company's own products and services, only if the user has given express consent; (iv) complying with applicable legal obligations.

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These General Terms govern the contractual relationship between LAND NETWORKING S.L. and the client in the provision of consultancy, development and implementation services for artificial intelligence solutions. Contracting any service implies full acceptance of these terms.

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LAND NETWORKING S.L. assumes no liability for the performance of the artificial intelligence models developed when used outside the context or data distribution defined in the project's technical specification. The results of AI systems are subject to inherent uncertainty and must not be used as the sole decision criterion in high-risk contexts without appropriate human oversight.

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8. Applicable Law and Jurisdiction

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Last updated: November 2026 · LAND NETWORKING S.L.