全球快递公司利用亚马逊Bedrock自动化货运异常处理

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