Reliability, SLOs, incident intelligence, predictive operations and self-healing across hybrid environments.
Talk to a SRE AIOps Specialist
Each service can be taken alone. They share telemetry, policy and runbooks when combined.
Unify multi-site and cloud telemetry, correlate incidents, find probable root cause and coordinate safe remediation.
Example scenario
Inject a faulty release, correlate several symptoms into one incident, approve a rollback and verify SLO recovery.
Detect recurring failures and run policy-controlled remediation with verification.
Example scenario
Force a service failure, recommend a runbook, approve it, recover the service and show the evidence.
Forecast capacity exhaustion and SLO risk before static thresholds are breached.
Example scenario
Simulate disk and traffic growth and alert before the real failure threshold is reached.
Score code, IaC and Kubernetes changes, control progressive delivery and roll back unhealthy releases.
Example scenario
Release a bad version, detect the canary regression and roll back automatically.
Prove DR readiness, backup restorability, failover and RTO/RPO through safe experiments.
Example scenario
Fail a primary site, recover to the secondary, validate the data and generate DR evidence.
Every service is built as a local-first lab, then extended to your private cloud and to AWS, Azure or GCP.
Ingestion health, processing latency, decisions, automation success and failure, overrides and false positives where measurable.
Validated, allowlisted actions with approval rules and least-privilege identities.
Healthy, false-positive, policy-blocked, automation-failure, rollback and telemetry-source-failure cases, all exercised.
Retained evidence for every decision, a short demo script, and setup, troubleshooting and cleanup documentation.
Rules every AIOps action follows, in every track.
AI output never goes straight to a shell, cloud API or cluster. It selects from validated runbooks.
Disruptive actions wait for a person. Low-risk, reversible steps can run on their own once you agree the rule.
Every action is followed by a health check, with rollback or compensation when the check fails.
Signals, scores, model or rule versions, approvals and results are retained and reviewable.
Adapted to the tools you already run.
Combine tracks to share telemetry, policy and runbooks.
Tell us what you run and where operations hurt most. We will suggest where to begin.
Talk to an AIOps Specialist