Leading tech-enabled 3PL reduces payment disputes by 40% by replacing their BPO with AI

This 3PL’s payment problem

This leading digital freight marketplace is a tech-enabled 3PL that connects shippers and carriers through its platform, which has modern features like automated pricing and booking. The company also handles essential 3PL work, such as carrier compliance, payments, and customer support.

They work with a BPO team to interpret the information packets that carriers send after a shipment has been delivered. Every carrier sends its information differently, so the team has to review all the documents carefully. They pull out invoices, bills of lading, rate confirmations, proof of delivery, and notices of assignment.

The BPO processes invoices from carriers so the 3PL can pay them, while the 3PL bills and collects payment from shippers. This gets tricky when there are payment disputes. If shippers disagree with charges, the 3PL doesn't get paid. Meanwhile, shippers get frustrated when they feel shipments aren't going smoothly. 

This 3PL ran into exactly this problem — they were dealing with too many disputes. This meant it took longer to approve payments, and their customers weren't happy.

Why BPO mistakes happened

The daily reality of BPO work reveals why mistakes can happen. Carriers send over documents in all kinds of conditions — invoices might have data on them that’s not clean or legible or that’s in a different place for every carrier, making it easy to make mistakes.

These examples show how this 3PL’s process unintentionally broke down in the hands of their BPO.

• Payment processing: The BPO team had to distinguish between different codes and costs on every invoice. Base charges, fuel surcharges, accessorial fees, and various types of discounts all need separate handling. A discount that looks like a surcharge or a misread number can create problems later.

• Contract verification: Delivery timestamps come in all formats, from neat digital prints to hasty handwriting in the corner of a form. The team needs to decipher these accurately while applying specific policy rules about timing and signatures for each contract.

The overall challenge is that carriers, shippers, and internal teams all handle things differently. A process that works perfectly for one carrier might fail for another. 

When payment disputes happen, untangling what went wrong means reviewing all these variable elements. Policy changes make everything harder — the team has to unlearn established habits while maintaining accuracy with their current work. 

How this 3PL replaced BPO payment processing with Reindeer AI

When the 3PL replaced their BPO with AI to handle payment approvals, Reindeer AI knew getting started wouldn’t be as simple as feeding process documentation into a model. These documents often miss the hidden layer of how work actually gets done — the unwritten rules and practices that flowed between human agents.

Process mining through rapid agent feedback

Instead of asking agents to write out their decision-making process, Reindeer AI created a zero-shot model and to them AI’s results and collected simple thumbs-up or thumbs-down feedback. These quick 20-second reviews revealed how agents actually made decisions rather than following formal procedures.

By capturing real-world decision-making instead of relying on documentation, accuracy quickly improved, and Reindeer started exceeding human performance benchmarks. Now, instead of waiting for a BPO team to split and process carrier documents, AI does it instantly and accurately.

The project showed that successful AI deployment isn't just about building models — it's about understanding how work actually gets done, not how it's supposed to be done.

Reindeer AI dramatically lowers disputes and increases CSAT

After working with Reindeer AI for just a week, disputes dropped by 40%, from 5% to 3%. Approval times went from 20 hours to just 3 minutes, and customer satisfaction scores increased by almost one point.

But switching to AI did more than just boost performance. It made regular measurement of these processes possible for this 3PL. The system uses manually tagged documents as its north star, measuring accuracy for each field and across all documents, with extra weight on the fields that matter. 

A random 2-5% of documents still get human eyes on them to keep AI honest. The system tracks accuracy and recall, ensuring no fields get missed. Add it all up, and you get an extraction grade currently sitting at 99% for this 3PL.

This beats the old way of measuring BPO work by miles. 3PLs typically spot-check their BPO's work whenever they can spare the resources, which isn't nearly as often as they'd like. When they do manage to run these audits, they usually find error rates around 30%. When you find those errors, you can only iterate on your process and hope it improves.

BPOs also try to run their own quality checks by slipping in duplicate tickets—processing 105 instead of the actual 100, for example—but this gets expensive quickly and rarely happens consistently. Without good software tools, measuring quality accurately is a constant headache.

AI turns this whole picture upside down. Every document automatically generates performance data. The system tracks real-time accuracy, spots escalations patterns, and shows how things are trending over time—all without adding extra work. 

The best part is that each correction makes the system better permanently. Unlike human processors who might forget and fall back into old habits, when the AI learns something, that knowledge sticks around, making the system sharper over time.