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IBS Software uses Amazon Bedrock for bilingual NER in cargo logistics

By PulseAugur Editorial · [1 sources] ·

IBS Software developed a bilingual Named Entity Recognition (NER) system for cargo logistics using Amazon Bedrock. The system processes English and Japanese emails to extract 23 types of information, such as air waybill numbers and delivery instructions. By employing knowledge distillation from Amazon Nova Pro to Amazon Nova Lite via Amazon Bedrock, IBS Software achieved a 95.085 percent F1-Score accuracy while reducing operational costs by 14x. AI

IMPACT Demonstrates how cloud-based AI services can be leveraged for specialized industry tasks, potentially reducing costs and improving efficiency.

RANK_REASON This is a case study of a company using a cloud provider's AI service for a specific business problem, not a release from a frontier AI lab.

Read on AWS Machine Learning Blog →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

IBS Software uses Amazon Bedrock for bilingual NER in cargo logistics

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Tool
This is a case study of a company using a cloud provider's AI service for a specific business problem, not a release from a frontier AI lab.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product, infra
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High
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Story freshness
99 days old
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Full methodology in our editorial standards.

COVERAGE [1]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Manu Raj L S ·

    Building bilingual NER for cargo logistics with Amazon Bedrock

    In this post, we share the technical approach using token-based distillation, lessons learned, and deployment architecture. If you face similar bilingual NER challenges, you can benefit from IBS Software’s experience with the Amazon Bedrock knowledge distillation capabilities.

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