Anomaly detection at 3σ
Statistical baselines + ML score deviations; only high-signal anomalies enter the pipeline.
🇺🇸 Dallas
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.
Dallas-Fort Worth carries one of the largest data-center concentrations in North America. Multi-tenant operators run SentienGuard agents at fleet scale to keep MTTR contractually defensible.
A representative 500-node Team deployment is approximately $24,000/year. Pro tier estimate: 500 nodes × $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 — SOC 2 / HIPAA / PCI-DSS / GDPR evidence native.
Slack, PagerDuty, Datadog, Prometheus, Grafana, OpsGenie, AWS, GCP, Azure, Kubernetes day one.