Normalized claim
Time: 1-2 hours
Investigation time fell from roughly 10 hours to 1-2 hours.
United Express & Caylent build an agentic AI observability solution on AWS, reducing issue resolution time by ~90%
Use case typeIT operationsUpdated Jun 13, 2026
United Express worked with Caylent to build an agentic AI observability solution for its weight-and-balance system. The system ingests millions of log entries, uses Amazon CloudWatch for near real-time alerting, and uses Amazon Bedrock to support conversational investigation of events and API data. The goal was to detect anomalies earlier and reduce manual log analysis across regional airline operations.
- Organization
- United Express
- Industry
- Logistics
- Location
- United States
- Published
- June 2026
Reported outcomes
+90%
timeTime & speed
1-2 hourstime
Strategic outcomes
Speed & agilityResolved issues before departuresNew product / capabilityBuilt conversational observability analysisBetter decisions & insightEarlier anomaly detection and analysisEmployee experienceEngineers shifted to higher-value work
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 90% increase
Mean time to detect and resolve issues improved by about 90%.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- United Express
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS Solutions Case Study
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 4
- 1Agentic AI
- 2Observability
- 3AIOps
- The weight-and-balance system generated about 8 million log entries per day across multiple external sources.
- Engineers spent roughly 336 hours per month on manual log analysis and often found issues only after operations were affected.
- Caylent helped validate the observability gap and build an AI observability solution on AWS.
- Amazon CloudWatch routes alerts to dashboards and messaging applications when thresholds are exceeded.
- An AI agent built on Amazon Bedrock lets engineers query event and API data in plain language and analyze emerging error patterns through a conversational interface.
Technologies
- Investigation time fell from roughly 10 hours to 1-2 hours.
- Mean time to detect and resolve issues improved by about 90%.
- Core support engineers spent less time searching and more time on higher-value work.
- The airline can resolve weight-and-balance issues before they affect departures.
Architecture
The solution combines Amazon CloudWatch for near real-time monitoring and alert routing with an AI agent on Amazon Bedrock that analyzes event and API data through a conversational interface. It integrates with existing KPI and application dashboards to provide a unified operational view for the weight-and-balance system.
Implementation partners1
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 10, 2026Publisher: AWSEvidence: VendorConfidence: Medium
AI-generated summary. Verify important details with the linked sources before relying on this case.
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