Resolve Kubernetes Incidents
AI-powered incident detection and root cause analysis for Kubernetes. Detect errors, analyze code context, and resolve issues before they impact your customers.
Root Cause Analysis: checkout-prod namespace
Analyzing 6 services. AI investigates, eliminates the noise.
postgres-mainfinally block. Connections not released on error pathKubernetes incidents are costing your team
Traditional monitoring tools show you what went wrong, but not why. Your engineers waste hours every week correlating logs, reading code, and debugging issues manually.
Hours wasted debugging
DevOps teams spend 10+ hours per week manually scanning logs and debugging production incidents.
Missing critical issues
Infrastructure problems like pod crashes go undetected until they impact customers.
On-call engineer burnout
Engineers pulled from sleep for incidents that could be automatically analyzed and prioritized.
Slow incident resolution
High MTTR (Mean Time To Recovery) due to lack of automated root cause analysis.
AI-powered incident management that understands your code and service dependencies
PulseStream automatically detects Kubernetes errors and infrastructure issues, deduplicates related incidents, and analyzes your entire codebase and system context to pinpoint the root cause in minutes.
Chat naturally with PulseStream to investigate any incident without context switching between logs, GitHub, dashboards, and documentation.
From deployment to resolution
in minutes
PulseStream fits into your existing Kubernetes workflow with zero friction.
Deploy the Agent
Install the PulseStream agent in your Kubernetes cluster with a single command. Outbound-only connections mean zero firewall changes.
No LoadBalancer, no public IPs, no inbound rules
Auto-Detect Incidents
PulseStream continuously monitors pod logs and infrastructure state. Errors are captured, deduplicated, and grouped automatically.
CrashLoopBackOff, OOMKilled, log errors: all detected
AI Analyzes Root Cause
AI analyzes your codebase, service dependencies, and error context to pinpoint the exact root cause, not just symptoms.
40K+ lines analyzed in under 60 seconds
Get a Fix Suggestion
PulseStream identifies the exact file and code path causing the issue and suggests a fix. Your team resolves incidents 10x faster.
Code-level fixes, not generic alerts
Stop debugging. Start resolving.
See how PulseStream compares to traditional monitoring tools and manual debugging workflows.
| Capability | PulseStream AI-Powered | Traditional Tools Datadog, PagerDuty, etc. | Manual Debugging kubectl + logs |
|---|---|---|---|
| Automatic incident detection from K8s pods | |||
| AI-powered root cause analysis | |||
| Actionable code-level fix suggestions | |||
| Service dependency mapping | |||
| Natural language incident investigation | |||
| Deploy in under 5 minutes | |||
| No inbound firewall rules needed | |||
| Data stays in your infrastructure |
When you run production systems at scale, incidents aren't rare, they're inevitable. What frustrated me wasn't the outages themselves, but how inefficient and noisy the debugging process had become.
At Qwilt, as the organization scaled and systems became increasingly complex, finding the true root cause often meant jumping between logs, dashboards, services, and code while the clock was ticking and customers were waiting.
PulseStream exists to change that. By understanding how code and services actually fit together, it helps engineers focus on fixing problems instead of fighting tooling.