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Revolutionizing Logistics Document Processing with Scale Document AI
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Education
- Transportation
Applicable Functions
- Logistics & Transportation
Use Cases
- Computer Vision
- Last Mile Delivery
Services
- Data Science Services
- Training
The Challenge
Flexport, a technology platform for global logistics, was facing a significant challenge in processing logistics documents such as bills of lading, commercial invoices, and arrival notices. These documents, which are critical for clearing shipments past customs and establishing ownership of goods, were traditionally processed using template-based and error-prone OCR (optical character recognition) solutions or manual labor. This method was not only time-consuming but also prone to errors, leading to delays in cargo movements and slowing down internal operations. Flexport realized the need for a machine learning-based document processing solution that could automate the process and extract valuable information accurately in seconds. However, the challenge was to find a partner with deep expertise in AI and machine learning who could operationalize this solution without Flexport having to build out a team of machine learning engineers or data scientists.
About The Customer
Flexport is a technology platform for global logistics. Their mission is to make global trade easy for everyone by combining technology, analytics, and logistics infrastructure with hands-on supply chain expertise. They handle a large volume of logistics documents such as bills of lading, commercial invoices, and arrival notices, which contain critical information for clearing shipments past customs and establishing ownership of goods. Flexport aims to avoid delays in cargo movements and accelerate internal operations through automation.
The Solution
Flexport partnered with Scale Document AI, which provided fine-tuned machine learning models that required no templates to process logistics documents at high accuracy and low latency. Scale Document AI's models extract information from logistics documents with over 95% accuracy and continue to improve over time as they process higher volumes of data. The models rely on Computer Vision and Natural Language Processing base models, which are further fine-tuned by annotating a small sample of Flexport's documents. This increases accuracy levels significantly and allows for the automation of handling shipping documents within seconds. The models are capable of understanding the layout of the document, its hierarchy, and the meaning of every field, making them resilient to layout changes, table boundaries, and other irregularities that thwart traditional template-based OCR and RPA systems.
Operational Impact
Quantitative Benefit
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