Meet SLOs, reduce MTTR, eliminate alert fatigue, and empower your SRE teams with AI-powered observability and automated remediation through Applicare and ArcIn.
Site Reliability Engineering is the discipline of running production systems with the rigor of software engineering — SLOs over guesswork, automation over toil, learning over blame. Done well, every incident becomes a signal that strengthens the platform. Done poorly, it becomes a pager that ages an engineer. Applicare gives SRE teams the AI-powered observability and automated remediation they need to protect SLOs without staying up to do it.
Availability SLO on checkout-svc hits a 14-day burn rate of 12× budget. At this rate the quarterly error budget exhausts in 18 hours. Most monitoring tools haven’t alerted yet — the absolute error rate is still within thresholds.
IntelliSense flags it. The shape is unusual: errors clustered on a single canary host running deploy v2.4.1, rolled out 22 minutes ago. ArcIn surfaces the burn-rate trajectory and the affected workload.
IntelliTrace maps the errors to connection-pool exhaustion in OrderRepository. Pool size was set to 20 by the deploy; baseline was 50. ArcIn explains: “Connection pool size decreased in commit a47f9d2. Throughput exceeded capacity within 8 minutes of canary promotion.”
IntelliTune matches the pattern to a known runbook: roll back the canary, restore the previous pool size. The action passes your policy gate (canary-only rollback is auto-approved). Traffic rebalanced. Burn rate drops back to nominal. Zero pages fired. SLO intact. Engineer sleeps through it.
| Pager + dashboards | Observability + manual runbooks | Applicare | |
|---|---|---|---|
| Detection | Threshold alerts | SLO burn rates (manual) | IntelliSense behavioral, <1s |
| Root cause | Engineer’s investigation | Dashboard-stitching | IntelliTrace causal, <60s |
| Remediation | Page someone | Engineer runs runbook | IntelliTune executes, policy-gated |
| SLO tracking | Spreadsheet | Separate SLO tool | Built-in, burn-rate aware |
| Alert fatigue | High | Medium | Low (correlated, ranked) |
| Engineer workflow | Wake up, investigate | Wake up, run playbook | Review PR or notification |
By default, no. Every remediation is gated by your existing approval rules — PagerDuty escalation policy, change-management workflow, or custom policy. Low-risk patterns can be configured to auto-apply (canary rollback, pod restart, connection-pool resize) with a full change-history record.
SLOs are configured per service against any tracked metric — availability, latency p99, request error rate, or a custom SLI. Error budgets compute automatically against your selected window. Burn-rate alerts use multi-window thresholds and surface at the speed of customer impact, not at end-of-quarter.
Yes — PagerDuty, Opsgenie, Splunk On-Call, Slack, Microsoft Teams, and webhooks for custom systems. ArcIn explanations attach to the page itself, so on-call engineers see the likely cause before they open a dashboard.
Yes. The 200+ pre-built runbooks ship out of the box; custom runbooks are authored as code (Python, Go, or shell) with declared inputs, gates, and rollback paths. Runbook execution shows up alongside the incident timeline.
First signals flow within an hour of pointing your OpenTelemetry Collector at Applicare. ArcIn answers questions immediately. IntelliTrace causal reasoning improves as the entity graph fills in — typically meaningful by day 2, fully populated by week 1.
Yes. AWS, Azure, GCP, on-premises Kubernetes, bare-metal — all in one causal graph. Cross-cloud service maps surface dependencies your architecture diagrams miss.