AI in transport and logistics — document agents

Automatic reading of transport documents

Process automation

Problem: Transport and billing documents are retyped by hand

How it works: OCR reads data from documents, and n8n flows pass it to operational systems

What you gain: Data from CMR and billing documents reaches the system without retyping — the forwarder checks rather than enters

Assistant for complaints and requests

Process automation

Problem: Handling client complaints and requests takes too long

How it works: The assistant analyses the request history in the RAG knowledge base and prepares a draft reply for review; the Decision Learning Layer proposes automating repetitive decisions

What you gain: The client gets an answer faster, and over time repetitive complaints are handled according to approved rules

Automatic reports for clients and carriers

Analytics and reporting

Problem: Summaries for clients and carriers are compiled by hand every month

How it works: Scheduled prompt runs (RRULE) that generate periodic reports

What you gain: Monthly summaries generate themselves, in the same format, on time — no month-end in spreadsheets

An assistant that uses the knowledge of dispersed branches

Knowledge and documents

Problem: Dispersed branches work in local mail and Teams, and knowledge does not reach headquarters

How it works: A local agent reads Outlook, Teams and folders without loading them into the central base; integration with Microsoft 365

What you gain: A branch’s knowledge is available to the assistant where it is created — without a project to migrate data to headquarters

Controlled automation of multi-step processes

Process automation

Problem: Operational processes require multi-step automation while keeping control

How it works: If/then rules in natural language; A2A delegation with approvals and limits; stateful orchestration of multi-step agent processes

What you gain: The process runs step by step through agents, with approvals where needed — and with the full state between stages

Scaling a successful pilot across the organisation

Adoption and skills

Problem: An AI pilot in one branch does not spread to the rest of the company

How it works: A “scale or stop” gate with a Cognitive SLA (F4); scaling in waves and an AI system catalogue (F5); AI Champions in every branch (F3)

What you gain: A successful pilot has a ready path to the next branches: in waves, with local leaders and a catalogue of proven use cases

Secure AI for drivers’ and clients’ data

Security and sovereignty

Problem: Drivers’ and clients’ data must not reach an external model

How it works: Personal data detection and masking; routing by data sensitivity

What you gain: Drivers’ and clients’ data is masked automatically, and sensitive queries go only to a local model

How do you transfer data from orders and CMR documents to the TMS?

Document agents in TWIN:DESK read data from documents and prepare it for the system, and a human approves the result before it is saved

How do you keep drivers’ and customers’ data secure?

Through a single gateway to models with personal data masking (PROXY:AI) and permissions by branch

Where should the decision be left to a human?

Wherever the outcome affects a customer or a driver: accepting a complaint, contractual penalties, carrier evaluation

How does AI help car dealers and service centres?

TWIN:DESK answers advisors’ questions from technical documentation, bulletins and warranty terms with the source cited, and agents prepare warranty claims and complaint responses. A human decides whether to accept a warranty claim