Preventive Analysis
Don't wait for alerts. NeuBird can proactively scan your infrastructure for issues before they become incidents — and analyze code changes before they reach production.
Proactive health checks
Run /health regularly to catch problems while they're still cheap to fix:
> /health
NeuBird scans incidents, alarms, error logs, metrics, and recent deployments to produce a structured Good/Bad/Ugly report. See Health Sweeps for details.
Pre-deployment risk analysis
Ask NeuBird to evaluate what could go wrong before merging or deploying a change:
> What pending PRs or recent merges are most likely to cause problems?
NeuBird cross-references code changes with production telemetry — checking current load, historical incidents, related services, and dependency health — then tells you which changes look risky and why.
More examples:
> We're deploying a new version of the payment service — what should we watch for?
> What could break if we scale down the worker pool from 10 to 5 replicas?
> The auth service is getting a major refactor in PR #412 — what production signals should we baseline before merging?
How it works
NeuBird uses the same investigation engine for preventive analysis as it does for reactive incident response. The difference is timing — you're asking the questions before something breaks, not after.
For code change analysis, NeuBird:
- Queries recent PRs and merges from your GitHub/GitLab integration
- Identifies which services and components are affected
- Checks current production health for those services (error rates, latency, resource usage)
- Looks at historical incidents involving the same services
- Assesses risk based on change scope, service criticality, and current production state
When to use preventive analysis
| Scenario | What to ask |
|---|---|
| Before a deploy | "What's the current health baseline for the payment service?" |
| During a change freeze review | "What risky changes merged in the last 48 hours?" |
| Monday morning check | "/health 72h" (weekend lookback) |
| Before scaling changes | "What happens to latency if we reduce replica count?" |
| Release readiness | "Are there any silent failures or degradation trends we should address before the release?" |