Observability & AIOps

Build self-healing IT

Close the loop: detect, decide, act. Observability that drives automation, not dashboards no one reads.

What you get
Alerts that mean something, tied to SLOs, not gauges.
Known failures get fixed automatically, with a human in the log.
Lower MTTR and less on-call load.
The problem

Tools generate signal. Humans correlate it. Alerts fire. By the time someone responds, the customer has already noticed. Most observability stacks are read-only.

  • MTTR plateaus, no matter how many tools you buy.
  • On-call fatigue shows up in attrition long before it shows up in surveys.
  • Postmortems all read the same: 'alert fired, response delayed, root cause obvious in hindsight.'
How it works

How we go about it.

01

Instrument

OpenTelemetry across services, infrastructure and network, one signal model, not three.

02

Alert on SLOs

Alerts tied to user-visible impact. Symptom-based, not cause-based.

03

Detect

AIOps anomaly detection where it earns its keep: high-cardinality, low-signal spots humans can't watch.

04

Act

Closed-loop remediation: known-good responses run automatically, with humans in the audit trail.

The stack

Tools we trust.

OpenTelemetryGrafanaPrometheusLokiPagerDutyRundeck
Self-healing is mostly boring. It's the same five runbooks, well automated, freeing people for the sixth.
Sasho Ristovski
Sasho Ristovski
DevOps Consultant · Stockholm
Customer case
Real time
signal straight to chat
Viaplay

Ready-made configurations for observability and log management across Viaplay's hybrid Kubernetes. A lightweight FinOps solution wired straight into the team's chat catches cost anomalies the moment they happen, not at next month's report.

Read the full case
See it live

Book a demo of build self-healing it

30 minutes, digital. We show the solution in practice and what it would do in your environment, no sales pitch.