How a logistics company cut approval time by 70% with AI

Our client — a logistics company processing more than five thousand freight orders per day — was facing a severe bottleneck at the order approval step. Each order required checking twelve different data points before confirmation. With a team of four, they could only process two thousand orders per day.

The first challenge when deploying the AI agent was not technical — it was data normalization. Orders arrived from seven different channels, each with its own format. We spent the first three weeks building just the input normalization pipeline.

The agent was designed with a rule-based architecture combined with ML: hard rules (invalid addresses, wrong postal codes, weight exceeding limits) are handled deterministically; grey-area cases (incomplete addresses, unusual values) are routed through a classification model.

In the second week of the pilot, we discovered the agent was making errors on a specific type of order: rural deliveries with non-standard address formats. We had to add a dedicated processing layer for this group.

After twelve weeks in full production: automated approval rate reached eighty-three percent, average processing time dropped from forty-five minutes to thirteen minutes, and error rate fell from two percent to zero point four percent.

The most important lesson: don't try to automate one hundred percent from day one. Design the system so the agent handles the easy cases and humans handle the hard ones — then continuously shift that threshold as the agent improves over time.

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