"AI Monitoring" isn't one feature — it's three that work together: ETA prediction warns before a threshold is crossed, anomaly detection flags a deviation from a resource's own normal, and RCA explains what's actually happening in plain language. This page is the map; the two deep-dive pages below are the detail.
A trend line fit on recent history, projected forward — a short-range "about to tip over" warning before a threshold is crossed.
A self-learning EMA baseline per resource, per metric — flags a real deviation from what's normal for that specific server, container, or pod.
Deterministic root-cause explanations for every signature hit, plus a chat assistant that answers questions grounded in live data.
Full methodology for ETA prediction and anomaly detection lives on the Predictive Alerts page.
The ETA prediction is a least-squares trend fit projected a few minutes forward — powerful for "is this about to become a problem," useless for "will we need more capacity next quarter." The anomaly detection is a per-resource statistical baseline, not a deep model trained on your whole fleet's behavior. The RCA is deterministic pattern-matching for known signatures, with an LLM stepping in only for the lines that don't match anything known. None of that is a knock — it's what makes each layer explainable and trustworthy, which is the actual point.
Full methodology for ETA prediction and baseline anomaly detection.
The tool-calling assistant and log-line explanation fallback.
How a predicted or detected anomaly becomes part of a named incident.
Offices in India and the US — reach out and we'll get back to you.
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