How an AWS team detects dashboard content failures at scale using Amazon Bedrock

This article describes how an AWS team built an automated content validation solution using Amazon Bedrock to detect dashboard failures at scale across hundreds of dashboards.

• Monitors hundreds of dashboards hourly to detect blank, stale, or incorrect content that infrastructure monitoring misses
• Uses two parallel AI validation mechanisms: visual integrity checks via Claude models and numeric consistency cross-checks
• Five-stage serverless architecture: scheduling, screenshot capture with redaction, AI analysis, alert routing, and telemetry persistence
• Detected 802 content failures in 30 days (0.52% of checks) with mean time to detection reduced from 72 hours to under 1 hour
• Key production lessons: design against false positives first and use deterministic code for numeric verdicts instead of LLMs

The solution enables BI teams to fix content issues before users encounter them, combining semantic AI tasks with deterministic logic for precision-critical decisions.