AWS Develops Automated Dashboard Content Validation Using Amazon Bedrock

Organizations relying on business intelligence dashboards often face silent failures where visual or data inaccuracies go unnoticed, despite infrastructure health reports. These failures can include blank visuals, stale data, or incorrect numbers, which pose risks especially when AI systems generate narratives based on this data.

To address this, AWS developed a last-mile automated content validation system that actively monitors dashboards hosted on the AWS Insights application. The system employs large language models (LLMs) on Amazon Bedrock to visually analyze dashboard sections and detect anomalies such as missing visuals or incorrect data. It also performs numeric consistency checks across different dashboards, ensuring data integrity.

The architecture comprises five serverless stages, including scheduled validation cycles, screenshot capture with redaction for privacy, AI analysis for visual and numeric validation, alert routing via Slack, and telemetry storage for trend analysis. Visual validation uses Amazon Bedrock models to identify structural anomalies, while numeric validation employs a hybrid approach combining LLMs and deterministic code for precise comparison.

Operational results over 30 days showed the system performed 153,000 checks, detecting 802 content failures, with a mean time to detection reduced to less than an hour. The system's design emphasizes minimizing false positives and separating semantic understanding from arithmetic verification to maintain trust.

Future enhancements include cross-dashboard consistency checks, automated remediation suggestions, and predictive analytics to preempt issues. This approach exemplifies how foundation models and serverless orchestration can improve data reliability at scale, supporting enterprise AI and analytics initiatives.