From Diesel to Data: Logistics Prediction and Text-to-SQL Pipelines with AWS AI

In global commodity trading, a single-day nomination delay costs thousands, yet estimating logistics for hundreds of kilotons is still a manual bottleneck.

We replaced this with a serverless pipeline using SageMaker and custom-trained model. To let users query data without writing a single line of code, we built a chatbot using Amazon Bedrock and Athena that converts natural language into SQL queries.

This session breaks down the architecture and deployment of these systems.