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AmexSoft.

Logistics

Logistics AI Assistant

Supports dispatch and ops teams with shipment status answers, exception triage and routine customer updates.

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Logistics AI Assistant

Logistics

Overview

What this solution explores

This reference architecture outlines an ops assistant that reads shipment state from systems of record, triages exceptions, and drafts customer updates — with humans owning commitments and edge cases.

Business problem

Where work gets stuck today

Dispatch and customer ops answer the same status questions all day while real exceptions compete for attention. Updates are inconsistent and hard to audit.

Existing process

How the work usually moves now

  1. 01

    Customer or partner asks for shipment status

  2. 02

    Ops looks up TMS/WMS screens and message history

  3. 03

    Manual reply composed under time pressure

  4. 04

    Exceptions are spotted late when someone notices a stuck shipment

Proposed AI workflow

The intended operating path

  1. 01Resolve shipment identity and fetch live status from systems of record
  2. 02Answer routine status questions with structured, auditable replies
  3. 03Triage exceptions into reason codes for ops queues
  4. 04Draft customer updates for human approval when commitments are involved
  5. 05Never invent ETAs — only surface system fields or escalate

System architecture

Building blocks of the system

Read-only connectors to TMS/WMS/status APIs
Shipment resolution and authorization checks
Exception classifier with ops reason codes
Drafting layer for customer messages with approval gate
Audit log of lookups, drafts and sends

Integrations

Systems this design connects to

  • TMS / shipment tracking APIs
  • WMS or warehouse status feeds
  • Customer communication channel (email/portal)
  • Ops work queue / ticketing

Implementation approach

How we would pilot and harden it

Start with status Q&A before any automated customer send
Map exception reason codes with dispatch leads
Gate any commitment language behind human approval
Expand corridors/customers only after data quality is trusted

Potential business impact

Qualitative outcomes to validate on a pilot

These are directional benefits — not invented percentages, hours saved or testimonials.

Faster answers to routine shipment status questions
Earlier visibility of exception queues for ops
More consistent customer update language after approval
Measure on live lanes — never invent on-time or hours-saved figures

Visuals

Architecture and UI slots

Reserved slots for diagrams and mocks. Empty by design until assets are produced.

Ops console

Slot for shipment status + exception triage UI

Customer update draft

Slot for approval-gated customer message draft

Related services

Delivery capabilities behind this solution

AI Agents for BusinessBusiness Process Automation

Insights

Related insights

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