What RAG is in simple terms
RAG (Retrieval-Augmented Generation) is a way to connect a language model to a company's documents. Instead of "inventing" an answer from trained weights, the model first finds a relevant fragment in the corporate database (a tariff, a contract clause, a regulation) and then formulates the answer strictly from the source it found. The answer turns out to be grounded: it includes a link to the specific document so it can be verified. This is what fundamentally sets a business RAG assistant apart from a bare chatbot that can be confidently wrong: if there is no answer in the knowledge base, the system does not make one up and instead flags the question for expert escalation.
Which logistics pain point does a RAG assistant solve
Every day dispatchers and customer service operators in logistics answer the same questions: what is the tariff for a route, what documents customs requires for a specific pair of countries, what HS code a cargo has, what the warehouse acceptance rules are. This knowledge is scattered across tariff grids in Excel, PDF regulations, emails and the heads of two or three experts. When a question is non-standard, the operator escalates it to an expert — and the client waits.
The same pain exists in contract work: transport terms, liability zones, demurrage penalties, and claim submission deadlines are scattered across contracts with dozens of counterparties and customer SLAs. An answer "from memory" is the most expensive here - a mistake in a claim deadline or liability zone turns into direct losses.
What exactly you can do with this tool (per open sources)
Open vendor materials and industry reviews describe a reproducible set of scenarios for logistics:
- Customs requirements and documents. The system retrieves current export/import requirements from official sources and helps assemble a correct set of documents. The Logistics Viewpoints review directly describes retrieving «correct import/export documentation requirements from a government database» and auto-generating forms (Logistics Viewpoints, 2025).
- Tariffs and HS codes by route. RAG pulls «current tariff rates, HS code classifications, and trade restrictions» for a specific «from — to» pair, i.e. it answers tariff questions without manually searching the tables (Logistics Viewpoints, 2025).
- Contracts, claims, and SLA documents. The same mechanism works on transport contracts and customer SLAs: the assistant finds the relevant clause in a specific contract - claim submission deadline, cargo liability, demurrage penalty - and answers with a link to the document and the clause number. This is a standard RAG scenario of "asking corporate documents," and it does not require a separate technology.
- Operator answers based on SOPs and regulations. An employee asks the assistant a question, and it pulls the answer from «SOPs, real-time shipment data, and exception logs» — operating procedures, shipment data and exception logs (GetTransport blog).
- Traceability for compliance. A key property for a regulated industry: "Every AI-generated answer can be traced back to source documents for compliance and accountability" — every answer is tied to a source (GetTransport blog).
As real adopting vendors, the sources name Flexport (instant customs consultations and document checks), Project44 and FourKites (shipment event data), plus the built-in retrieval assistants in SAP and Oracle (Logistics Viewpoints, 2025).
What result it delivers for customer service
Industry reviews give benchmarks for the impact of assistants in logistics customer service:
- chatbots handle about 79% of basic questions of users and cut support costs by roughly 30%, cutting response time in half (Acropolium);
- among companies using such assistants, the source names UPS, DHL, FedEx, Maersk, C.H. Robinson, XPO Logistics, DB Schenker, Kuehne+Nagel (Acropolium).
DHL separately uses generative AI to explain import duties to customers for their shipments and to verify HS/HTS code classification (DHL Discover).
Important caveat: these are open industry data and vendor cases, not the result of a KT.Team deployment. We cite them as a benchmark for "what is achievable in principle with the tool".
How it works technically
The minimal RAG setup for a logistics knowledge base: connectors to source systems (tariff tables, PDF regulations, customs reference guides, contracts, SOPs) -> chunking and indexing in a vector database -> when an operator asks a question, the system finds the most relevant chunks -> the LLM formulates an answer strictly from them and attaches a source link. If a question is disputed or not found in the base, the system does not make anything up and instead flags it for expert escalation - in other words, it honestly acknowledges the limit of its knowledge.
Cloud or your own environment: what to consider under GDPR
The deployment choice depends on what is in the knowledge base. If it is tariffs, public regulations, and internal SOPs without personal data, it is faster and cheaper to start with a cloud LLM: the environment can be assembled in a short iteration, with usage-based pricing. If the documents contain personal data (customers, drivers, cargo owners) or trade secrets, such data must not go to an external provider, and the retrieval layer with the model should be deployed in your own environment: on-premise or in a CIS cloud, in line with GDPR requirements.
What matters is that the RAG architecture does not change - only where the model and vector database run. That means the deployment choice can be made during the audit and revisited later without rewriting the system. More on choosing a model for regulatory requirements in the analysis Foreign LLMs under GDPR: what is allowed and what is not.
Where the KT.Team approach fits
The main risk with such a solution is building a monolith where tariffs, customs rules and warehouse regulations are hardwired into one opaque service. KT.Team builds systems like this as loosely coupled architecture: knowledge base, source connectors, retrieval layer and LLM responder are separate, replaceable components. This delivers two measurable business effects. First — transferability: the solution can be handed between teams or vendors without a rewrite, because each source is plugged in through its own connector rather than fused into the code. Second — locality of changes: update a tariff grid or a customs rule, and you reindex only that source without touching the rest of the system.
The approach itself is AI-native: the agent does not just answer, it grounds every answer on a verifiable document and decides on its own when to hand a question to a human. This takes the load off the 2-3 experts who currently carry all the industry expertise and removes them from the critical path of the answer to the client.
Business outcome
Process being improved — dispatcher and customer service answers on shipping rules without escalations to experts. A RAG knowledge base for tariffs, contracts, customs rules, and warehouse procedures turns this process from "the operator asks an expert" into "the operator asks the system and gets a grounded answer with a source link." For the business, the result is measured not by "we implemented AI," but by the share of questions resolved without escalation, customer response time, and reduced dependence on subject matter experts.
If you want to try a RAG assistant on your process, start with the format AI for business: one process in a short iteration: choose one process, set up the environment, measure the impact, and scale only after that.
