Accelerating aircraft IFEC diagnostics with agentic AI on AWS

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Panasonic Avionics Corporation provides in-flight entertainment and connectivity (IFEC) systems across a large global fleet serving hundreds of airlines and billions of passengers annually. When a system issue affects passenger experience at this scale, engineers must diagnose the root cause quickly across thousands of unique deployment configurations. Doing this manually, correlating logs, metrics, and ticketing data across diverse fleet variants, can take hours and requires deep institutional knowledge. In this post, you will learn how Panasonic Avionics Corporation worked with AWS and the AWS Generative AI Innovation Center for architectural guidance to build an agentic AI system. The solution uses Amazon Bedrock, Amazon SageMaker, and AWS Glue to significantly reduce diagnosis time while maintaining high accuracy. Challenge: Evolving maintenance at scale Panasonic Avionics Corporation’s operational data serves as a critical asset for monitoring fleet IFEC health. The company relies on a data lake and data system built on AWS to store and organize operational data gathered from across its fleet, processing large volumes of data daily. Despite this robust data infrastructure, translating raw operational data into actionable diagnostics presented operational challenges at scale. Panasonic Avionics Corporation deploys services with configurations tailored to individual operational requirements. Each deployment generates unique log patterns, which complicates fleet-wide performance assessment. Teams performed manual reviews to correlate metrics across multiple …

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