Worldwide Express 部署 Bedrock 智能体处理货运账单异常
Worldwide Express 与 AWS、LTM 合作，利用 Amazon Bedrock 构建了连接 SpeedShip 运输管理系统的多智能体 AI 解决方案。该系统将账单异常分流至发票匹配、价格差异分析和附加费校验等专用智能体。智能体在提出建议前会检索承运商专属计费规则，成本最终确认及 AI 生成的说明则交由人工审核人员在 SpeedShip 分析师界面审阅。上线六个月后，Worldwide Express 报告称，该方案已部署至 40 多家 LTL 承运商，每周处理约 1 万条异常，错误率低于 1%，活跃异常减少 75%，回款周期改善 4 周，每年节省超过 100 万美元，并一次性释放 1000 万美元现金流。AWS Machine Learning Blog 将这些数据归属于该公司报告。
报告记录了一套已投入生产的多智能体流程，结合承运商专属知识、明确的决策边界、SpeedShip 集成与人工审批。报告还称，该方案使 FinOps 团队从处理常规异常转向解决更复杂的争议。
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.
#Worldwide Express#AWS#Amazon Bedrock#Amazon EC2#ShipStation Global#LTM#SpeedShip