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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.
IBS Software’s Cargo system processes thousands of bilingual cargo logistics email messages daily. The system extracts critical information such as air waybill (AWB) numbers, flight details, weights, and delivery instructions in both English and Japanese. This added to the complexity of building a robust Named Entity Recognition (NER) solution. Challenges included manual intervention that slowed operations and a trade-off between accuracy and cost. IBS Software needed an AI solution that could accurately identify 23 different entity types across two languages while remaining cost-effective at scale.
After exploring multiple approaches, IBS Software used managed distillation capabilities of Amazon Bedrock to create a production-ready solution. By distilling knowledge from Amazon Nova Pro into the more efficient Amazon Nova Lite model, IBS Software achieved 95.085 percent F1-Score accuracy while reducing operational costs by 14x. This case study details their journey from facing complex open-source implementations to a successful deployment on AWS that now processes cargo email messages in real time.
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.
The goal was to build a bilingual NER system capable of extracting 23 entity types from cargo logistics email messages written in English and Japanese. The key entities include:
The primary risks included maintaining high accuracy across both languages, managing inference costs at scale, and achieving low latency for real-time processing. With the model distillation capabilities of Amazon Bedrock, you can use smaller, faster, and more cost-effective models. These models deliver accuracy for your use case that is comparable to the most advanced models in Amazon Bedrock.
The following diagram shows the end-to-end bilingual NER workflow on Amazon Bedrock.