OTO-Customs enables Customs Brokers to improve the efficiency and accuracy of their document-based tasks while eliminating the reliance on manual data entry.
Intelligent Document Processing (IDP) revolutionizes the customs brokerage industry by enabling hyper-automation throughout the customs processing workflow.
Using state-of-the-art machine learning algorithms, the system provides predictive insights and actionable recommendations to supercharge productivity, including HS code assignment suggestions and exception management guidance.
Discover the Top 3 Reasons to choose OTO-Customs
Streamline Customs Clearance
Enhance Accuracy and Compliance
Unlock AI Insights and Predictions
Seamless Integration Enhancing Productivity and Improving Bottom Line
Process customs entries within minutes, for any mode of transportation, 24/7
Customs brokers face the challenge of accurately and promptly processing a high volume of diverse documents manually, as they work against time to meet shipment deadlines and avoid border hold-ups.
Reduce Shipment Processing Costs with AI
AI-powered automation streamlines customs clearance, reduces errors, expedites document processing, and ensures compliance, resulting in substantial cost savings and improved operational efficiency.
Our Partnership
OTO Customs was specifically designed to process international trade and customs documents
Bills of Lading
Commercial Invoices
Packing Lists
Additional Capabilities
Generating shipments in Descartes based on received documents
Reconciliation Engine for Customs Brokerage documents
Streamlining Request for Information (RFI) through automation
Customized business rules and workflows to expedite and enhance transparency throughout each process stage
Smart Inbox and Work Management with automated assignment
Intelligent Document Management with easy search on past data
Advanced Reporting and Business Intelligence
AI Prediction and Recommendations: Predicting critical fields corrections, HS code prediction and recommendations, insights on actions to be taken based on past actions