75% increase in operational efficiency with 80% reduction in resolution time

• The emails were highly unstructured, and critical information spanning over thousands of attributes had to be extracted for various email categories. The mails had to be analyzed and mapped in near real-time to reduce delays.
• The cargo-vessel mapping involved significant labor and had constraints because of the legacy systems. The mails had to be analyzed and mapped in near real-time with the available vessel to reduce delays.
• Adherence to regulatory compliance was difficult as they had international operations.

The entire email parsing workflow was automated with MobiEbot, our home-grown AI-powered email bot. Our email bot was customized for vessel and cargo container tracking. 2 Million+ emails were classified into various categories like vessel position, cargo order, noon report, and junk.

Our email bots used rule-based NER and NLP techniques for tracking cargo container and shipment information. Our email bots extracted 80+ data attributes, including cargo container number, vessel number, vessel tracking, and other shipment details. The extracted data were normalized and checked for accuracy with the automated feedback mechanism.

Each minute 40+ emails were classified, and critical data points were extracted. This classification helped the agents to map the cargo to the most suitable and available vessel in real-time. Data security and governance improved by establishing data policies and business rules.