中文 XIANGXIN
LLM-NATIVE CONTEXT

Reframing AI for logistics

Drawing on our experience in port container drayage, we are exploring how business facts, relationships, states and rules can be organized into logistics context agents can interpret, verify and trace.

MAPPING
AGENT CONTEXT Container drayage
CONTEXT GRAPH
CONTAINER
PORT
ORDER
APPROVAL
01 Business Semantics 02 State & Relationships 03 Authority & Accountability

LOGISTICS IN CONTEXT

Let LLMs understand
The logistics behind the data

Orders, containers, transport events and business rules become meaningful through their relationships. Models need both the current state and the path that led to it.

  1. 01
    S

    Semantics

    Align fields, states and events to shared business meaning

  2. 02
    C

    Context

    Connect orders, containers and port events across time

  3. 03
    B

    Boundaries

    Define what agents can access and where they can participate

BUSINESS BOUNDARIES

As AI moves deeper into operations
The lines of accountability must be clearer

Models can support interpretation and coordination, while business systems remain authoritative and permissions and execution stay controlled. Every critical decision retains a clearly accountable owner.

01

Source Grounding

Every judgment traces to a business fact

02

Bounded Access

Visibility and participation are governed separately

03

Human Judgment

Critical decisions retain a named owner

04

End-to-end Trace

Participation and outcomes remain reviewable

LOGISTICS + AI

OPEN DIALOGUE

TEULAB.AI

Ideas and collaboration

LOGISTICS + AI CONTACT

If you are exploring how AI can understand and participate in real logistics operations, we would like to hear from you.

CONTACT US [email protected]