Enterprise AI agents for logistics operations.

Agentic AI in logistics is software that is given an objective rather than a script. It reads incoming information — enquiries, carrier updates, documents, system records — decides what needs to happen next within limits you set, takes approved actions in your systems, and escalates anything outside its authority to a person. Common uses are quotation preparation, shipment exception monitoring, customs document checking, customer status updates and invoice preparation.

Traditional automation follows fixed rules: if this field appears, copy it there. It works well when everything arrives in the same format and breaks when it does not. Agentic AI can handle variation — an enquiry written as a paragraph, a carrier notice in a new layout, a document missing a field — because it reasons about the information rather than matching a pattern. In practice most logistics operations end up using both, with rules for the predictable steps and agents for the messy ones.

Usually the one that is high volume, repetitive, dependent on information scattered across systems, and has a clear definition of a correct result. For freight forwarders that is almost always quotation preparation or shipment exception monitoring. For 3PLs it is order exceptions and client reporting. For distributors it is capturing supplier updates into the ERP. We score your workflows in the first two weeks and tell you which one repays fastest.

A focused pilot on one workflow typically falls within our published USD 7,500–20,000 planning range. A full single-workflow production deployment commonly runs USD 20,000–60,000, and a multi-agent programme across several departments can exceed USD 60,000. The biggest cost drivers are how many systems the agent must touch, whether your carriers expose APIs, the quality of your rate and master data, and how much authority the agent is given.

A focused agent on one real workflow: 4–6 weeks from kickoff. A fully scaled deployment with guardrails, integrations and team training: about 90 days. You see live results on real shipments in the first phase, not a demonstration on sample data.

Usually yes. We build against whatever interfaces your TMS actually exposes — APIs, database access, file exchange or EDI — rather than assuming every platform is equally easy to connect. Where a system genuinely has no safe interface, controlled browser automation can bridge the gap if the workflow and the platform's terms allow it. We assess your real architecture in discovery instead of promising integration in advance.

That is the normal situation, not the exception. Much of the useful information in freight still arrives by email, PDF, portal screen or EDI file. Our shipment exception agents are designed to work from mixed sources: carrier portals, sailing schedules, terminal status, email notices and your own TMS records, normalised into one view. We document what each source can and cannot reliably tell you.

Only if you decide it should, and we would normally advise against it at the start. The usual pattern is that the agent prepares the complete quote and puts it in front of your salesperson for one-click approval. Once you have months of evidence on accuracy, you can widen its authority for defined lanes or customers — for example, standard rates below a value threshold — while everything else stays with a human.

Every agent has a confidence threshold that routes uncertain cases to a person, a guardrail layer that blocks any action outside policy, and a full log of what it read, decided and did. We design for being wrong, because it will be sometimes. The question is whether the system fails safely and visibly — and whether you can see exactly what happened afterwards.

Yes, but bilingual performance has to be tested against your actual documents and messages. Mixed-language enquiries, Arabic company names transliterated three different ways, scanned documents and regional address formats all belong in the evaluation set before go-live rather than being discovered in production.

Not necessarily. Deployment can be designed around your privacy, residency and contractual requirements using approved regional cloud infrastructure, a private environment or on-premise components. The right architecture depends on what data the agent touches and what your customer contracts commit you to — which is why we answer the data questions before designing anything.

That should not be the design goal, and it is rarely what happens. The realistic outcome is that the same team handles far more volume, spends its time on exceptions, customers and commercial judgement, and stops doing clerical retyping. In most of our deployments headcount stays the same and throughput goes up.

Start with the least authority that still creates value. Most agents should begin in watch-and-recommend mode, move to acting with human approval, and only earn independent action on defined case types after measured performance supports it. Widening authority should be a decision backed by evidence, not a setting someone turns on.

You do. Source code, prompts, orchestration logic, evaluation datasets and runbooks are produced in your repositories and belong to you. Third-party foundation model IP remains with its provider, as it does for everyone.

No. We choose the model, framework and hosting that fit your data residency, latency and budget, and we explain each choice to your team. If a cheaper or better-suited option exists, that is what you get. Nothing in the build is designed to make swapping providers difficult later.

The UAE is moving to a Peppol-based e-invoicing model, with a voluntary pilot phase from July 2026 and mandatory adoption phasing in from 2027 for larger businesses. Practically, invoicing automation built now should produce structured invoice data rather than PDFs, so it can feed an accredited service provider when your phase arrives. Dates have moved before — confirm the current schedule with the Ministry of Finance before you plan around it.

Some of it, and we will tell you which parts honestly. Agents are far more tolerant of messy input than rule-based automation, but they cannot invent a rate that was never recorded or a container number nobody entered. In discovery we separate "messy but usable" from "genuinely missing", and the second category becomes a piece of work with its own cost and timeline.

Production agents need monitoring. Carrier formats change, customers change how they write, and models get updated. We track success rate, exception rate, human review rate, latency, running cost and the business metric the project was justified on, keep prompts and tools under version control, and improve against a growing test set of real cases.

After any phase, with no cancellation penalty. The first phase is a fixed-fee assessment and you keep the findings whatever you decide. We make leaving straightforward on purpose — it is the clearest signal that we intend to earn the next phase.

Yes. We work across the UAE and the wider GCC, with delivery teams in Dubai, Riyadh and Trivandrum, and we also deliver for clients in Australia and the United States. Logistics workflows differ by market — customs, documentation and carrier behaviour especially — so we design against your corridor rather than a generic template.