Anomaly detection at 3Ο
Statistical baselines + ML score deviations; only high-signal anomalies enter the pipeline.
π©πͺ Berlin
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.
Berlin is Europe's startup capital β SaaS, fintech, mobility. Per-node flat pricing fits early-stage capital efficiency far better than per-metric observability bills.
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.
Statistical baselines + ML score deviations; only high-signal anomalies enter the pipeline.
1536-dim vector embeddings match incidents to playbooks in ~165 ms with ~95% accuracy.
High-confidence playbooks run autonomously; lower-confidence ones request Slack approval.
Every action re-checks the original signal; failed verifications roll back and escalate.
Hash-chained, append-only β SOC 2 / HIPAA / PCI-DSS / GDPR evidence native.
Slack, PagerDuty, Datadog, Prometheus, Grafana, OpsGenie, AWS, GCP, Azure, Kubernetes day one.