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 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.

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 .

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.

Figure 1: Worldwide Express’s AI-powered exception handling architecture on AWS

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

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.

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:

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.

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.