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