Accelerate Tariff Code Compliance

Code 10,000 Parts in One Second

 

Instant automated application of correct tariff codes.

Consistent, accurate and audited decision making process.

Institutionalize expert knowledge, capture it inside machine learning models.

 

Promptly react to changes in Customs rulings.

 

Instantly classify thousands of parts resulting from acquisitions.

 

Optimize the supply chain.

Tariff Code Assignment In Real Time

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The Servient Machine Learning Solution

  • Utilizes past data from any ERP system
  • Learns how the experts interpret the customs guidelines
  • Works with structured as well as unstructured attributes
  • Captures the patterns in Machine Learning models
  • No subjective inconsistencies or user fatigue

The Servient Machine Learning Solution

Machine learning algorithms integrate with your ERP system to operate on data in real time and predict the most likely HS codes with confidence levels. 

 

Accelerate the decision making process by narrowing the possibilities from tens-of-thousands of codes to a few of the most probable.

 

The solution analyses the various structured and unstructured attributes of a material's part from the ERP system and makes suggestions in real time. 

 

An audit of the user's comments and decisions is maintained for defensibility and compliance.

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Solution Workflow Overview

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 Be Proactively Compliant

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Create new machine learning models in order to modify the classifications on past data as soon as the customs categorization changes.

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Apply machine learning models to the ERP systems from new acquisitions and become compliant from day one.

Halliburton's Use of Servient's AI for Trade Compliance 

Servient's application of AI for trade compliance is a working example of how machine learning can be leveraged to save time and reduce costs.

A video of our CLOC 2018 session features this "real world" use case during an interactive discussion with Dixon Sabin, Director of International Trade Compliance at Halliburton.

View The Presentation

 

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