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)建造了大型的：全系统收入约50亿美元，超过75艘低于卡车载荷(LTL)的载货车和超过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逐，承运人追逐，有自己的规则来检查。 World Express公司外包给一个小组，该小组一次审理一起案件，为每起争端打电话给客户公司和承运人。 特例堆积而成，现金被卡住，每份订单都要花更长时间才能变成钱。
World Worldwide Express与AWS和LTM合作，用Amazon Bedrock搭建了多代理AI解决方案，直接插入他们的运输管理系统(TMS)"SpeedShip". 发布后6个月，该公司报告主动例外减少75%，时间到现金改善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货运中，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相匹配。 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：
- 线项不匹配：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
每个例外都引发了五步手动过程：
- 对照定购单、货物收据和定价，相互参照发票，确定例外类型。
- 研究供应商记录中的根本原因。
- 向承运人或仓库发送载有争议细节的电子邮件。
- 等待回应。
- 在TMS中执行决议。
由于每个航母都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)可以处理结构化的，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，比较与发运数据有关的收费，以及确定承运人、例外类型和美元数额是否有效。 这种可变性使问题成为适用于代理人工智能框架的因素。
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19个自通至Cash生命周期的自动化机会。 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，而Amazon弹性计算云(Amazon EC2)是引擎。 该设计要求精细地控制每个agent的原因，决定边界的位置，以及例外在agents之间的路由。 在Amazon EC2上直接构建框架，由Amazon Bedrock提供推论，将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、投入和决策界限明确和可审计。 在计费-争议工作流程中，每个自动决定都需要有解释。
Agents 时速
当发现发票例外时，系统会把它引向三个专业AI agents中的一个，每个专业处理特定类型的计费纠纷：
Agent 发现相匹配的运货记录
- 定价差异Agent将承运人的收费与合同费率进行比较，并确定差额是否有效。
- 进入验证Agent检查意外费用(例如住宅交付或升降门收费)是否得到实际交付活动的支持。
每一agent在提出建议之前会先抽取具体的承运人计费规则和政策。 例外解决后，系统生成一个平通语注解，解释发现什么，采取了什么行动。
每个自定义的agent都有一个指令即时(定义其作用，决定边界，和输出格式)，一组动作(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访问一个合适的地基模型，同时继承现有的AWS治理。 关于按区域分类的模型可用性，参见AWS区域在Amazon Bedrock中支持的模型。
行动
agents通过分析师使用的同一种API连接到SpeedShip. 这就是说，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电线进入了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计费规则、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，在作出决定前取回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。 同一三所agents处理所有航母，而没有任何航母专用代码。
人类的跳动
在初步推出期间，FinOps的收养必须经过批准。 最初，一名人机审查员将使用现有的"快船UI"在最终敲定之前批准建议。 最终当精度超过99%时，批量批准成为默认。
观察性
Worldwide Express的团队按载体构建了跟踪自动化率的仪表板，按例外类型，分辨率时间和人比频率计算的错误率。 当审查者拒绝推荐时，系统会登录反馈。 团队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。
端到端流
以下图显示为端到端流，从例外检测到三个专业的agents到人类认可。 在图中，检测出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在Amazon EC2上运行有"Auto Disprision"，使用agent框架，有自己的即时，工具和载体知识库，并调用Amazon Bedrock进行模型推论。 agents生成一份分辨率报告。 费用最后审定和一份人工智能笔记随后提交一个人文审查员，由SpeedShip分析员UI批准。
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目前每周接近一万(约50%)例外，误差率还不到1%. 这把FinOps团队的时间从例行解决转向复杂而高价值的纠纷。
显示的影响最明显地表现在积极例外的积压，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%。 Worldwide Express解锁了1000万美元自由出行的现金流量，超过$$$$$$$的年成本节省，60x的第一年ROI. 公司报告时间变现金增加四周。
AWS将超过40%的每周计费例外情况自动化，今天，我们每周发行大约1000万美元的客户账单，其速度是以前根本不可能的。
- Nathanael Pearson,FinOps的副总裁，世界快报
展望未来
世界快报已经将这种做法扩大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，前进的道路是起步小。 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分级。
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，载体特有知识基础的结合，以及"人入"的审批设计，让"环球快车"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订单到卡什流程。
探索类似方法是否对你的组织有用，请求与你的账户团队对话，或更多地了解Amazon Bedrock. LTM这样的合作伙伴可以用自己的数据运行一个"价值快跑"的证明。
本文由 AI宝库自动抓取并翻译，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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