What You Can Do with Skyflo

Decision makers search by problem, not product category. Here are the real operational challenges Skyflo solves — each running on the same Plan-Execute-Verify control loop.

Use Case 1 of 5

AI for Kubernetes Troubleshooting

When pods crash at 2 AM, you're switching between kubectl logs, describe, events, and Grafana — losing critical minutes correlating data across tools.

How Skyflo solves it

  • Natural language incident queries
  • Automatic log + event + metric correlation
  • Root cause analysis in plain English
  • Suggested remediation steps

Diagnose a CrashLoopBackOff in 30 seconds, not 30 minutes

Use Case 2 of 5

AI for CI/CD Automation

Jenkins pipelines fail silently, build logs are walls of text, and debugging requires tribal knowledge of your pipeline configuration.

How Skyflo solves it

  • Jenkins build management via natural language
  • Pipeline failure diagnosis with log analysis
  • Build triggering and monitoring
  • SCM-aware deployment insights

Identify and fix failed builds in one conversation

Use Case 3 of 5

AI for Incident Response

When production alerts fire, your team spends more time correlating signals across tools than actually fixing the issue. Context switching kills MTTR.

How Skyflo solves it

  • Cross-tool signal correlation (K8s + Helm + Argo)
  • AI-assisted root cause identification
  • Verified remediation with human approval
  • Post-incident verification that the fix holds

Cut incident resolution time from hours to minutes

Use Case 4 of 5

AI for Progressive Delivery

Canary and blue-green deployments require careful orchestration across Argo Rollouts, and a single misconfigured promotion can take down production.

How Skyflo solves it

  • Argo Rollouts management in natural language
  • Human gates on canary promotions
  • Automated rollback on failure signals
  • Analysis run monitoring and decisions

Safe canary promotions with zero manual Argo commands

Use Case 5 of 5

AI for Release Verification

After every deploy, someone has to manually verify that services are healthy, endpoints respond, and no regressions slipped through.

How Skyflo solves it

  • Post-deploy validation against intent
  • Drift detection between desired and actual state
  • Rollback readiness assessment
  • Automated health checks across services

Verify every release matches your intent — automatically

See Skyflo in Action

Every use case above runs on the same Plan-Execute-Verify control loop. See it operate on real infrastructure.