Industry · Export trade, forwarding, warehousing, transport

Trade, supply chain & logistics

Enquiries, documents, tracking and dispatch — AI watches the time zones and the typos.

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Pain points

What keeps owners up at night

Slow multilingual replies miss time-zone windows

Shipping and customs documents are error-prone

Order tracking lives in spreadsheets and chat groups

Slow response to dispatch and transit exceptions

Roles

Which roles AI transforms, and which it augments

“Transform”: AI executes, people spot-check. “Augment”: AI drafts and prompts, people decide.

Role	Mode	What AI does / what people keep
Sales (enquiries)	Augment	AI drafts multilingual replies and quotes; sales confirms
Documentation	Transform	AI generates and cross-checks documents; people spot-check
Customs prep	Augment	AI checks descriptions, HS codes and consistency
Order follow-up	Transform	AI tracks milestones and flags exceptions
Dispatcher	Augment	AI drafts vehicle and route plans

Department agents

AI employees we typically deploy

Enquiry Reply AgentDocumentation AgentCustoms Check AgentShipment Tracker AgentDispatch AgentReceivables Agent

Super AI Workbench

Order & logistics control tower: the workbench for this industry

Demo UI with placeholder numbers.

BOOK.AI · Order & logistics control towerDemo UI · placeholder numbers

Cockpit
AI employees
Workflows
Knowledge
Audit log

Revenue¥ ——

Labor cost¥ ——

Output / person——

Open exceptions——

AI task completion by department (illustrative)

Sales

Ops

Finance

Supply

AI assistant · Monday briefing

End-to-end milestones and delay alerts
Document errors and customs checks
Vehicles, routes and in-transit exceptions
Receivable days and collection reminders

Sample case

A sample case from this industry

A trade & logistics firm in the Yangtze River Delta (sample)

AI-generated

Sample caseTrade & supply chain

A trade & logistics firm in the Yangtze River Delta (sample)

1 hrFirst enquiry response (was 24 hrs)

−80%Document errors

60 → 40Documentation & follow-up roles

“We used to lose orders to time zones. Now enquiries sent at midnight are answered by morning.”— General manager (sample quote)

What we did in this sample case

01 Diagnose, 2 weeks: end-to-end order process breakdown
02 90-day pilot: enquiry replies and document preparation
03 Months 4–12: customs checks and receivables reconciliation

Read the full case →

FDE in practice

Public FDE practice in this industry

Third-party cases from public reports — unrelated to the BOOK.AI sample above — to show how this industry is approaching AI.

Case information is compiled from public reports; figures are as reported by the original sources, and all rights belong to their owners. BOOK.AI was not involved in any of these projects.

Image from the source page: General Mills — © Palantir; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientTrade, supply chain & logistics · US

General Mills × Palantir

FDE intensity: StrongFDE / long-term co-build (since 2019)

“$40,000/day in savings, or $14M annually”; “Over 70% of recommendations … are accepted”

As reported by Palantir · 2024-03 · provider-published

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Background

Staff made ~50 million supply-chain decisions a year driving ~$10B in cost of sales, largely by hand.

How the FDE work was done

Partners since 2019: first unified ~200 master-data and business tables, then built the ELF system on AIP with human-in-the-loop review.

Results (as reported by Palantir)

Over 70% of ELF recommendations accepted by humans; about $40,000/day or $14M a year saved, deployed on only part of the network.

Takeaways

Data foundation first, then AI recommendations with a human in the loop — a safe path for supply-chain decisions.

Provider: Palantir
Delivery mode: FDE / long-term co-build (since 2019)
Region: US

Read the original ↗Building an Intelligent AI-Driven Supply Chain at General Mills (AIPCon Impact Study)

English summary by BOOK.AI; figures as reported by Palantir. Quotation marks indicate wording from the source.

Screenshot of the source page (captured 2026-10-19): Tianjin Port Group — © Huawei (page screenshot; content © source) · Image source

Public case · not a BOOK.AI clientTrade, supply chain & logistics · China

Tianjin Port Group × Huawei

FDE intensity: MediumJoint release

Sample library of 6 categories, 80+ sub-classes and millions of samples

As reported by Huawei · 2024-06-22 · provider-published

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Background

Many port operations and complex objects; generic models did not work out of the box.

How the FDE work was done

Fused the Pangu model with port scenarios and industry samples, co-built a professional sample library and rolled out CV scenarios.

Results (as reported by Huawei)

Sample library covers 6 categories and 80+ sub-classes at million-sample scale.

Takeaways

In asset-heavy industries, an industry sample library — i.e. a data inventory — comes first.

Provider: Huawei
Delivery mode: Joint release
Region: China

Read the original ↗Original title (Chinese): 天津港集团联合华为发布3项数字化转型成果 - 华为

English summary by BOOK.AI; figures as reported by Huawei. Quotation marks indicate wording from the source.

Screenshot of the source page (captured 2026-10-19): SF Technology (SF Holding) — © Sina Finance / original author (page screenshot; content © source) · Image source

Public case · not a BOOK.AI clientTrade, supply chain & logistics · China

SF Technology (SF Holding) × Volcano Engine

FDE intensity: MediumCo-build (training & inference platform)

Throughput of the Fengyu model up to 5x

As reported by Sina Tech · 2025-07-04 · media report

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Background

High-concurrency logistics made training and inference cost and stability the barrier to scale.

How the FDE work was done

SF trained and deployed its Fengyu models on Volcano Engine’s ML platform, with prefill/decode separation and dynamic traffic scheduling.

Results (as reported by Sina Tech)

Training performance 30%+ over open-source frameworks; throughput up to 5x; used in 20+ business scenarios.

Takeaways

At scale, compute and inference cost become the next bottleneck — plan for cost optimisation.

Provider: Volcano Engine
Delivery mode: Co-build (training & inference platform)
Region: China

Read the original ↗Original title (Chinese): 携手火山引擎，顺丰科技用AI重塑供应链|火山引擎|顺丰科技|供应链_新浪科技_新浪网

English summary by BOOK.AI; figures as reported by Sina Tech. Quotation marks indicate wording from the source.

Image from the source page: CSX — © Microsoft; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientTrade, supply chain & logistics · US

CSX × Microsoft

FDE intensity: MediumCo-build

“In the first 45 days of operation, over 1,000 customers consulted the AI assistant across more than 4,000 conversations.”

As reported by Microsoft · 2025 · provider-published

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Background

Rail-freight customers needed real-time shipment tracking and service information; the portal was cumbersome.

How the FDE work was done

CSX used Copilot Studio to load knowledge articles quickly and Azure AI Foundry to pull live data from several back-end systems.

Results (as reported by Microsoft)

In the first 45 days, 1,000+ customers used the assistant across 4,000+ conversations.

Takeaways

A B2B customer portal is a natural first agent for logistics: clear demand, data already there.

Provider: Microsoft
Delivery mode: Co-build
Region: US

Read the original ↗CSX boosts supply chain agility using Microsoft Copilot Studio and Azure AI Foundry | Microsoft Customer Stories

English summary by BOOK.AI; figures as reported by Microsoft. Quotation marks indicate wording from the source.

Screenshot of the source page (captured 2026-10-19): Oujian Group (Yunmaotong platform) and its forwarder clients — © Yangpu District Government, Shanghai (page screenshot; content © source) · Image source

Public case · not a BOOK.AI clientTrade, supply chain & logistics · China

Oujian Group (Yunmaotong platform) and its forwarder clients × Oujian Group (in-house, offered as a service)

FDE intensity: LightProvider product delivery

Customs-declaration cost down more than 50%

As reported by Yangpu District Government, Shanghai · 2025-11-14 · government site

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Background

Customs documents were keyed in manually, one shipment at a time — slow and error-prone.

How the FDE work was done

Oujian built an AI document tool into its Yunmaotong platform that recognises and enters documents for forwarders.

Results (as reported by Yangpu District Government, Shanghai)

Declaration cost down 50%+; at one mid-size forwarder, entry time per shipment from 15 to 3 minutes and labour cost −40%.

Takeaways

Trade documents are high-volume and rule-based — a strong first AI employee for exporters.

Provider: Oujian Group (in-house, offered as a service)
Delivery mode: Provider product delivery
Region: China

Read the original ↗Original title (Chinese): 欧坚集团研发的“云贸通”平台智能制单工具亮相报关成本直降50%_上海市杨浦区人民政府

English summary by BOOK.AI; figures as reported by Yangpu District Government, Shanghai. Quotation marks indicate wording from the source.

See public FDE practice across all 14 industries →

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