Anomaly Center

ML-driven anomaly detection over every metric stream — learns per-server baselines, flags deviations before they become incidents.

Home / Anomalies

Predictive Intelligence

Adaptive Baselines

Per-metric baseline windows across 1h / 6h / 24h / 168h with trend-aware thresholds.

1/6/24/168hTrend-aware

Anomaly Dashboard

Overview cards, severity timelines, per-server breakdowns and drill-down context.

OverviewBy-server

Predictive Failure

Holt-Winters forecasting with 2h lookahead. >85% probability triggers preventive incidents.

ForecastPreventive

AI Validation

LLM validates flagged anomalies — separates genuine problems from transient noise.

LLMNoise reduction

SYNOTI in Production

0
Background Services
auto-restart · Docker
0
Prometheus Exporters
real-time metrics
0%
Self-Heal Rate
~90% auto-recovered
0
Endpoints Benchmark
100 GB/day

What Teams Say

SYNOTI cut our MTTR from hours to under a minute for routine failures — the self-healing engine resolves most issues before my team even sees a ticket.
Operations Director
Financial Services
Air-gap readiness was the deciding factor. All 33 services run on our hardware with zero egress — exactly what our compliance team required.
Chief Information Security Officer
Government Sector
From Telegram ChatOps approvals to AI root-cause analysis, the platform fits how our SREs already work. Deployment took less than 15 minutes.
SRE Lead
Telecommunications

Common Questions

Adaptive per-metric baselines across 1h/6h/24h/168h windows with trend-aware thresholds.
Yes — Holt-Winters forecasting with 2-hour lookahead triggers preventive incidents above 85% probability.
An LLM validates flagged anomalies and separates genuine problems from transient noise.
Air-Gap ReadyZero TelemetryWazuh 4.x XDRMITRE ATT&CK15 SOAR Actions230+ REST APIs5 RBAC RolesMTTR < 60s

How It Works

End-to-end pipeline diagrams from the SYNOTI engine.

Predict issues before they hit

Deploy SYNOTI on your infrastructure today — air-gapped, self-healing, AI-native.