Deep statistical analysis and event forecasting for infrastructure

Which 20% of incidents cause 80% of the downtime, so you fix the few that matter. The Gini coefficient (a measure of inequality: 0 - all incidents equally bad, 1 - one incident caused everything) shows how concentrated the problem is.

Groups incidents by behaviour - time of day, duration, frequency - instead of by ticket category. A long list of "different" errors often turns out to be a small number of root causes.

Finds hidden repeat cycles in uneven time series - a method from astrophysics applied to incident logs. It also shows in which hours and days failures cluster. It reports FAP (false alarm probability - the chance the cycle is random noise).

Tests whether a deploy or a config change really moved the incident rate, and by how much. Cliff's delta (effect size - how big the shift is, from 0 to 1) turns "it feels worse" into a number.

Estimates how many failures to expect in the coming weeks, and the probability of a rare but very long outage - the one that breaks an SLA. The answer is always a range, never a falsely precise single number.

Finds which events appear together and in what order, so an early alert can sit on the leading signal instead of the crash itself. Lift shows how many times more often a pair occurs than by chance.

Mean time between failures (MTBF) and mean time to repair (MTTR) straight from your export - no agent, no access to your servers. Mean, median and P95 (the value only 5% of cases are worse than) together show the bad day, not only the typical one.

Puts your metrics next to the thresholds of your industry. The thresholds are data, not a hard-coded list. Where a threshold is unknown, the engine says so instead of inventing a norm.