Reducing GenAI Monitoring by 70% with AWS

Situation

A leading U.S.-based airline was running multiple GenAI applications across different business use cases, each producing varied outputs and serving distinct objectives. While GenAI adoption was accelerating, there was no standardized way to monitor model quality, safety, or reliability.
Industry-standard evaluation metrics were insufficient, and building custom monitoring pipelines for each application would have required significant engineering effort, time, and cost -slowing innovation and increasing operational risk.

Problem

• No centralized monitoring framework for GenAI quality, safety, and usage
• Absence of industry-standard metrics aligned to business context and semantics
• High variability across GenAI applications, requiring flexible evaluation logic
• AWS Bedrock service quotas limiting throughput at scale
• Transient LLM errors causing pipeline failures and manual intervention
• High DevOps effort to onboard and maintain monitoring for new GenAI use cases

Solution

Mu Sigma built a cloud-native GenAI monitoring framework on AWS  to standardize quality, safety, and Responsible AI across applications—without rebuilding pipelines for each use case.

A config-driven architecture on Amazon S3 and Amazon Aurora enables new GenAI applications to be onboarded through configuration changes alone. Business-aligned evaluation goes beyond generic ML metrics, using custom logic such as NLP metrics, semantic similarity, and LLM-as-a-Judge powered by AWS Bedrock.

AWS Step Functions, Lambda, and ECS Fargate orchestrate quota-aware processing and automated retries, ensuring reliable scale and maximum Bedrock throughput without throttling.

By combining decision science, cross-industry context and engineering rigor we integrated hard to twire responsible AI into the airline’s operating model.

Impact:

• Established a standardized, reusable GenAI monitoring framework with business-aligned custom metrics
• Built a scalable, AWS-native architecture aligned with cloud best practices – 100% AWS Bedrock quota utilization without throttling
• ~90% automated recovery of transient failures, cutting failed alerts by 60–80%
• ~70% reduction in DevOps effort for new monitoring use cases