AI Ops & Intelligent Automation
Your team is drowning in alerts. Smart filtering, automatic incident grouping, and AI-drafted post-mortems with full audit trails. Narrow, scoped automation for your specific outages, not a 'platform' you have to learn.
AI Ops is the most marketing-laden term in DevOps right now, so Perpelin defines it narrowly: specific automation capabilities applied to the metric/log/incident triad, with audit trails and explicit confidence scoring.
Capabilities currently shipped in production: anomaly detection on metric streams using Prometheus and Grafana Cloud, log-based incident classification using OpenTelemetry traces, LLM-driven root-cause analysis that produces a draft incident report a human reviews, and self-healing Kubernetes operators for narrow, well-understood failure classes.
Engagements scope the capability list before the SOW. Nothing ships to production without a human-in-the-loop review path. We don't sell 'AI-powered observability' as a single deliverable; we sell specific automations against your specific incidents.
Who this fits
Ideal client
- · On-call teams burning out on noisy alerts
- · Mature observability stack with months of incident history
- · Companies with a Kubernetes platform and a real incident-review cadence
Not a fit
- · Teams without baseline observability (build that first)
- · Buyers wanting a chatbot that answers 'what went wrong'
- · Anyone shopping for an AIOps platform (we build, we don't resell)
Sample engagement
Week 1: incident-history review, signal selection. Weeks 2–4: anomaly-detection rollout on selected services. Weeks 5–6: incident classification + draft RCA generation. Weeks 7–8: handoff with documented operator behavior. ~8 weeks fixed-fee.
Production outcomes
Tell us what you're building.
Send a project brief and we'll reply within one business day, or book a 30-minute intro call directly.
Thanks, got it.
We'll reply within one business day at the email you provided. A real person reads every message; no auto-responders.