Anomaly detection at 3σ
Statistical baselines + ML score deviations; only high-signal anomalies enter the pipeline.
🇺🇸 New York
AIOps Platform in New York
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
Fintech and financial-services density in New York drives strict NYDFS Part 500 and SHIELD Act controls. Trading-floor and clearing-house infrastructure has near-zero tolerance for MTTR, making autonomous resolution a direct trading-revenue protection mechanism.
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.
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 — SOC 2 / HIPAA / PCI-DSS / GDPR evidence native.
Stack integrations
Slack, PagerDuty, Datadog, Prometheus, Grafana, OpsGenie, AWS, GCP, Azure, Kubernetes day one.