Anomaly detection at 3Ο
Statistical baselines + ML score deviations; only high-signal anomalies enter the pipeline.
π¨π Zurich
AIOps Platform in Zurich
An AIOps platform (Artificial Intelligence for IT Operations) ingests telemetry from infrastructure and applications, applies machine learning to detect anomalies and correlate signals, and acts on those insights to keep systems healthy. Modern AIOps platforms execute remediation autonomously instead of merely paging an on-call engineer. SentienGuard detects infrastructure incidents, selects a remediation playbook, executes controlled fixes, verifies recovery and records the evidence.
Local operating context
Swiss financial-services FINMA outsourcing circulars require demonstrable continuous controls on the operations layer. Autonomous remediation produces the evidence inline.
A representative 500-node Team deployment is approximately β¬22,320/year. Pro tier estimate: 500 nodes Γ $4/endpoint/month annual commit. FX rates as of 2026-05. See /pricing for canonical USD pricing.
Capabilities
RAG-based playbook selection
1536-dim vector embeddings match incidents to playbooks in ~165 ms with ~95% accuracy.
Confidence-gated execution
High-confidence playbooks run autonomously; lower-confidence ones request Slack approval.
Verification and rollback
Every action re-checks the original signal; failed verifications roll back and escalate.
Immutable audit log
Hash-chained, append-only β SOC 2 / HIPAA / PCI-DSS / GDPR evidence native.
Stack integrations
Slack, PagerDuty, Datadog, Prometheus, Grafana, OpsGenie, AWS, GCP, Azure, Kubernetes day one.