除了Amazon Bedrock之外，环球快车如何实现货运自动化

人工智能

除了Amazon Bedrock之外，环球快车如何实现货运自动化

环球快车(ShipStation Global的一部分)建造了大型车辆：全系统收入约50亿美元，超过75艘低于卡车载货量(LTL)的运输船和超过13万客户。 但同样的尺度有隐藏的成本。 每个月，1 500万美元的免费现金流被冻结，被锁在账单中，但还没有人解决。 每一个都必须由手追击，由承运人追击，并有自己的规则来检查。 World Express外包给一个小组，一个案件一个接一个，给客户和每个争端的承运人打电话。 特例堆积起来，现金被卡住，每个订单都要花更长的时间才能变成钱。

Worldwide Express与AWS和LTM合作，用Amazon Bedrock制造了一种多剂AI解决方案，直接插入他们的运输管理系统(TMS)SpeedShip. 发布6个月后，该公司报告主动例外减少75%，时间到现金增加4周，每年节省100万美元，一次性免费注入1 000万美元现金。

并分享结果。

挑战

在LTL运费中，大约14%的发票没有按照定购单、货物收据和承运人发票之间的标准三程匹配。 当匹配失败时，发票进入例外处理。 就世界快车而言，例外分为三类：

- 行项不匹配：与货运记录不符的数量或服务代码。

- 定价差异：承运人开具了不同于合同价格的账单。

- 意外的准入费：升降门费、住宅附加费

每个例外都触发了五步手动程序：

- 对照定购单、货物收据和定价，相互参照发票，确定例外类型。

- 研究供应商记录的根源。

- 向承运人或仓库发送带有争议细节的电子邮件。

- 等待一个回应。

- 在TMS中执行决议。

随着每个航母在自己的关税结构和争议工作流程下运行，这一过程消耗了FinOps团队的大部分带宽。

基于规则的流程自动化(RPA)可以处理结构化，可预测的工作流程。 然而，例外解决办法需要根据具体情况作出判断：解释承运人的准入政策，比较与装运数据相比的收费，以及确定承运人、例外类型和美元数额是否有效。 这种可变性使问题适合代理人工智能框架。

从评估到生产

2025年6月，环球快车与AWS业务转型与价值咨询团队合作，确定整个订单至卡什生命周期的19个自动化机会。 了解例外自动化的商业价值，环球快车在不到6个月内从评估转向生产。

如何运作

溶液运行于AWS. Amazon Bedrock提供AI推理，Amazon弹性计算云(Amazon EC2)为引擎。 设计要求精细控制每个agent的理由，决定边界的位置，以及例外在agents之间的路由。 直接在Amazon EC2上构建框架，由Amazon Bedrock提供推论，使这种管弦逻辑完全掌握在团队手中，并支持开源 agent库。 对业务逻辑的生产控制使每个agent的作用、投入和决定界限明确和可审计。 在计费-争议工作流程中，每个自动决定都需要有可解释性。

兹Agents

当发现发票例外时，系统将它引导到三个专业AI agents中的一个，每个处理特定类型的计费纠纷：

Agent 发现的发票

- 定价差异Agent将承运人的收费与合同费率相比较，并确定差额是否有效。

- 进入验证Agent检查意外费用(如住宅交货或升降门收费)是否得到实际交货活动的支持。

每个agent在提出建议前会先抽取具体的承运人计费规则和政策。 例外解决后，系统会生成一个简单的语言说明，解释发现什么，采取了什么行动。

每个定制的agent都有指示即时(定义其作用，决定界限，和输出格式)，一套动作(集成到SpeedShip)，以及访问特定载体的知识库。 通过使用Amazon Bedrock，每个agent通过单个API访问一个合适的基础模型，同时继承现有的AWS治理。 对于按区域分类的模型可用性，参考AWS区域在Amazon Bedrock中支持的模型。

行动

agents通过分析师使用的相同的API连接到SpeedShip. 这意味着大赦国际确切地看到一个审查人员会看到什么(合同费率、运输细节、交货证明)，并将其分辨率写入同一个工作流程。 speedShip在AWS上拥有API，因此agents电线进入与其他现有系统相同的端点。

载体知识库

每个承运人都有不同的收费规则、收费定义和争议程序。 World Express存储每个承运人的文件：关税规则、附加费定义、争议步骤和差异阈值。 agent处理例外时，它会在作出决定之前检索该具体承运人的政策和标准作业程序文件，因此每项决定都以承运人自己的规则为依据。 同一三台agents处理所有航母，而没有任何航母专用代码。

人类的跳动

在初始推出期间，FinOps的收养需要批准步骤。 最初，一名人文审查员会使用现有的SpeedShip UI在最终敲定建议前批准建议。 最终当精度超过99%时，批量批准成为默认。

观察性

Worldwide Express的团队建造了仪表板，当审查者拒绝推荐时，系统会登录反馈。 该小组根据拒绝历史完善教学提示和知识基础，稳步扩大自动化覆盖面。

端对端流量

以下图显示的端对端流，从例外检测到三个专业的agents到人类的认可。 在图中，检测到数据来源的例外情况(SpeedShip TMS、一个货运系统、一个电子数据交换(EDI)服务器以及载体和供应商门户)，并将其传送到三个agents中的一个。 每个agent运行在Amazon EC2上带有Auto Usization，使用agent框架具有自己的即时，工具，以及载体知识库，并调用Amazon Bedrock进行模型推论。 agents制作解析报告。 费用最后审定和一份AI笔记然后提交一个人文审查员在SpeedShip分析器UI中审批。

结果

6个月后，环球快车在40多艘LTL航母上部署了解决方案。 agents目前每周接近10,000(约50%)的例外，误差率不到1%. 这把FinOps团队的时间从例行解决转向复杂的高价值纠纷。

显示的影响最明显地上升了积极例外的积压，从2025年6月的1 500万美元下降到6个月后的4 000美元，减少了75%。 环球快车解锁了1万美元自由现金流，超过1万美元的年度成本节约，60x第一年ROI. 公司报告时间变现金增加四周。

AWS将超过40%的每周计费例外自动化，今天，我们每周发行大约1 000万美元的客户账单，其速度是前所未有的。

- Nathanael Pearson,FinOps的副总裁，世界快报

展望未来

环球快车公司已经在将这一办法推广到秩序至卡什周期的其他部分。 对于管理高载运量的物流公司来说，前进的道路是启动小型。 首先将最重复的例外类型自动化，证明准确性，然后在投资组合的其余部分进行规模化。

结论

Worldwide Express的例外自动化显示当代理AI遇到范围很广的商业问题时，Amazon Bedrock，载体专用知识库的结合，以及人性化的Loop批准设计，让World Express在不到6个月的时间里从评估转向生产，同时保持财务控制。 公司现在正在将自动化扩展到其他的顺序到卡什流程。

探索类似方法是否对你的组织有效，请求与你的账号团队对话，或更多地了解Amazon Bedrock. LTM等合作伙伴可以用自己的数据运行一个价值冲刺的证明。

本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。

Artificial Intelligence

How Worldwide Express automated freight exceptions with Amazon Bedrock

Worldwide Express (part of ShipStation Global) had built something big: approximately $5 billion in systemwide revenue, over 75 less-than-truckload (LTL) carriers and more than 130,000 customers. But that same scale had a hidden cost. Every month, $15 million in free cash flow sat frozen, locked in billing exceptions no one had resolved yet. Each one had to be chased down by hand, carrier by carrier, with its own rules to check. Worldwide Express outsourced to a team that worked cases one at a time, calling customers and carriers for each dispute. The exceptions piled up, cash stayed stuck, and every order took longer to turn into money.

Worldwide Express worked with AWS and LTM to build a multi-agentic AI solution with Amazon Bedrock that plugs directly into their transportation management system (TMS), SpeedShip. Six months after launch, the company reported a 75 percent reduction in active exceptions, a 4-week improvement in time-to-cash, $1M annual savings, and a one-time $10M free cash flow infusion.

In this post, we describe the challenge, explain how Worldwide Express designed the multi-agent solution, and share the results.

The challenge

In LTL freight, roughly 14 percent of invoices fail the standard three-way match between the purchase order, the goods receipt, and the carrier invoice. When a match fails, the invoice enters exception handling. For Worldwide Express, exceptions fell into three categories:

- Line-item mismatches: Quantities or service codes that didn’t align with the shipment record.

- Pricing variances: The carrier billed a rate different from the contracted price.

- Unexpected accessorial charges: Liftgate fees, residential surcharges, or reweigh adjustments that weren’t on the original quote.

Each exception triggered a five-step manual process:

- Identify the exception type by cross-referencing the invoice against the purchase order, goods receipt, and pricing.

- Research the root cause in vendor records.

- Send email to the carrier or warehouse with dispute details.

- Wait for a response.

- Execute the resolution in the TMS.

With each carrier operating under its own tariff structure and dispute workflow, this process consumed most of the FinOps team’s bandwidth.

Rule-based process automation (RPA) can handle structured, predictable workflows. However, exception resolution requires contextual judgment: interpreting a carrier’s accessorial policy, comparing charges against shipment data, and deciding validity given a carrier, exception type, and dollar amount. That variability is what made the problem a fit for the agentic AI framework.

From assessment to production

In June 2025, Worldwide Express engaged the AWS Business Transformation & Value Advisory team to identify 19 automation opportunities across the Order-to-Cash lifecycle. Knowing the business value of exceptions automation, Worldwide Express moved from assessment to production in less than 6 months.

How it works

The solution runs on AWS. Amazon Bedrock provides AI reasoning, and Amazon Elastic Compute Cloud (Amazon EC2) is the engine. The design calls for fine-grained control over how each agent reasons, where decision boundaries sit, and how exceptions are routed between agents. Building the framework directly on Amazon EC2, with Amazon Bedrock providing inference, keeps that orchestration logic fully in the team’s hands and supports open source agent libraries. Production controls of business logic make each agent’s role, inputs, and decision boundaries explicit and auditable. In a billing-dispute workflow, every automated decision needs to be explainable.

Agents

When an invoice exception is detected, the system routes it to one of three specialized AI agents, each handling a specific type of billing dispute:

- Invoice Matching Agent finds the matching shipment record and flags what doesn’t line up.

- Pricing Variance Agent compares the carrier’s charge against the contracted rate and determines whether the difference is valid.

- Accessorial Validation Agent checks whether unexpected fees (such as residential delivery or liftgate charges) are supported by the actual delivery events.

Each agent pulls specific carrier billing rules and policies before making a recommendation. After the exception is resolved, the system generates a plain-language note explaining what was found and what action was taken.

Each custom agent has an instruction prompt (defining its role, decision boundaries, and output format), a set of actions (integrations into SpeedShip), and access to carrier-specific knowledge bases. By using Amazon Bedrock, each agent accesses a suitable foundation model through a single API while inheriting existing AWS governance. For model availability by Region, refer to Supported models by AWS Region in Amazon Bedrock.

Actions

The agents connect to SpeedShip through the same APIs that analysts use. That means the AI sees exactly what a human reviewer would see (contract rates, shipment details, delivery proof) and writes its resolution into the same workflow. SpeedShip has APIs on AWS, so the agents wire into the same endpoints as other existing systems.

Carrier knowledge base

Every carrier has different billing rules, fee definitions, and dispute procedures. Worldwide Express stores each carrier’s documentation: tariff rules, surcharge definitions, dispute steps, and variance thresholds. When an agent processes an exception, it retrieves that specific carrier’s policies and standard operating procedure (SOP) documents before deciding, so each decision is grounded in the carrier’s own rules. The same three agents handle all carriers without any carrier-specific code.

Human-in-the-loop

The approval step was necessary for FinOps adoption during the initial rollout. Initially, a human reviewer would use the existing SpeedShip UI to approve recommendations before they’re finalized. Eventually when accuracy exceeded 99 percent, bulk approval became the default.

Observability

Worldwide Express’s team built dashboards tracking automation rate by carrier, error rate by exception type, resolution time, and human override frequency. When a reviewer rejects a recommendation, the system logs the feedback. The team refines instruction prompts and knowledge bases based on rejection history, steadily increasing automation coverage.

End-to-end flow

The following diagram shows the end-to-end flow, from exception detection through the three specialized agents to human approval. In the diagram, exceptions from the data sources (the SpeedShip TMS, a shipping system, an electronic data interchange (EDI) server, and carrier and vendor portals) are detected and routed to one of three agents. Each agent runs on Amazon EC2 with Auto Scaling, uses an agent framework with its own prompt, tools, and carrier knowledge base, and calls Amazon Bedrock for model inference. The agents produce a resolution report. Cost finalization and an AI-written note then go to a human reviewer for approval in the SpeedShip analyst UI.

Results

Six months in, Worldwide Express has deployed the solution across over 40 LTL carriers. The agents currently close 10,000 (approximately 50 percent) exceptions weekly with a less than 1 percent error rate. This shifted the FinOps team’s time from routine resolution to complex, high-value disputes.

The impact shows up most clearly in the backlog of active exceptions, which fell from $15M in June 2025 to $4M six months later, a 75 percent reduction. Worldwide Express unlocked $10M in freed-up cash flow, over $1M in annual cost savings, and 60x first-year ROI. The company reports time-to-cash improving by four weeks.

“With AWS, we’ve automated more than 40 percent of weekly billing exceptions, significantly reducing costs and accelerating billing cycles from days to minutes. Today, we release approximately $10 million more in customer billings each week at a speed that simply wasn’t possible before.”

— Nathanael Pearson, VP of FinOps, Worldwide Express

Looking forward

Worldwide Express is already expanding this approach to other parts of the Order-to-Cash cycle. For logistics companies managing high carrier volumes, the path forward is to start small. Automate the most repetitive exception type first, prove accuracy, then scale across the rest of the portfolio.

Conclusion

Worldwide Express’s exception automation shows what’s possible when agentic AI meets a well-scoped business problem. The combination of Amazon Bedrock, carrier-specific knowledge bases, and a human-in-the-loop approval design let Worldwide Express move from assessment to production in less than 6 months while maintaining financial controls. The company is now expanding automation to other Order-to-Cash processes.

To explore whether a similar approach might work for your organization, request a conversation with your account team or learn more about Amazon Bedrock. Partners like LTM can run a Proof of Value sprint with your own data.

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