A global logistics operator was weighed down by manual processing of letters of credit and shipping documents, with teams spending half their time on paperwork instead of strategic work. Slow, labour-intensive handling of scanned PDFs and images created bottlenecks that delayed throughput and strained resources. By deploying AI-powered intelligent document processing and end-to-end RPA automation, the company transformed its workflow—saving over 890 hours per month and boosting processing efficiency by 60%. The result: staff freed from repetitive tasks, faster document cycles, and significantly improved service levels.

Case Study Source: Svitla Systems

Problem Statement

A logistics operator was losing time to manual handling of shipping paperwork, especially letters of credit and related documents, which slowed processing and tied up staff.

Goal

Automate the processing of letters of credit and shipping documents to cut manual effort and accelerate throughput while boosting overall process efficiency.

Challenges

Teams in logistics spend about **50%** of their working hours on manual document management tasks.

Letters of credit and shipping documents were handled manually, making the process slow and labour‑intensive.

Key data was embedded in scans, PDFs, and images, requiring extraction before it could be used downstream.

Actions

Deployed AI‑enabled RPA with intelligent document processing (OCR, NLP, and machine learning) to capture and extract document data.

Automated end‑to‑end handling of letters of credit and shipping documents to reduce manual entry and handoffs.

Streamlined document workflows to speed data capture and enable quicker downstream processing.

Impact:

Freed staff from routine paperwork to focus on higher‑value work.

Faster document cycles improved operational tempo and service levels.

The Challenge

A logistics company found itself drowning in paperwork. Staff spent roughly half their working hours simply managing documents by hand. Letters of credit and shipping papers, in particular, created a significant bottleneck.

The problem was clear: every document arrived as a scan, PDF, or image. Before anyone could actually use the information, someone had to manually pull out the key details. This labour-intensive approach meant slow turnaround times and teams stuck doing repetitive admin work instead of more valuable tasks.

The Solution

The company turned to automation, specifically bringing in AI-powered technology to handle the heavy lifting. The solution combined robotic process automation with intelligent document reading capabilities—think optical character recognition and machine learning working together to understand and extract data from documents.

Rather than just automating bits and pieces, they redesigned the entire workflow from start to finish. Documents now move through the system automatically, with data captured and routed onwards without human intervention.

What Changed

Time Back in the Day

The numbers tell a compelling story. The company reclaimed over 890 hours each month that staff had previously spent processing letters of credit and shipping documents. That’s the equivalent of adding several full-time employees without actually hiring anyone.

Faster, Smoother Operations

Processing efficiency jumped by 60%. Documents that once crawled through the system now zip through much faster. Shorter cycle times mean the entire operation runs at a better pace.

The Bigger Picture

Beyond the raw statistics, the transformation changed how the team works day-to-day. People who once spent their mornings typing data from scanned documents can now focus on problem-solving, customer service, and other work that actually requires human judgement.

Quicker document processing also means better service. When paperwork moves faster, shipments move faster, and customers notice the difference.

This case demonstrates how automation isn’t just about cutting costs—it’s about freeing people to do work that matters whilst simultaneously improving operational performance.