IBS Software cuts cargo AI inference costs by 14× with Amazon Bedrock distillation

What happened

IBS Software built a bilingual named-entity recognition system for cargo logistics email processing in English and Japanese, using Amazon Bedrock's model distillation to compress Amazon Nova Pro into the smaller Nova Lite model. The distilled Nova Lite achieved 95.085 percent F1-Score accuracy while reducing production inference costs by 14×.

Why it matters

Cargo logistics companies handle thousands of bilingual messages daily, extracting critical data like air waybill numbers, flight details, and weights. The traditional trade-off between accuracy and cost made this difficult; IBS Software's approach shows that managed distillation can deliver near-teacher performance (retaining 98 percent of the teacher's accuracy) at a fraction of the operational expense, potentially enabling similar multilingual NER deployments across industries that need cost-effective, real-time processing.

What to watch

The system identified 23 entity types across two languages and showed a 2.565 percent accuracy gap on Japanese text versus English, primarily due to complex kanji combinations and smaller Japanese training data (150 versus 350 email messages). IBS Software addressed this by augmenting Japanese training data with synthetic examples and applying post-processing rules for known patterns.