Know before it breaks
and routing anomalies — before your clients notice anything.
How it works
Four independent statistical models analyze every entity continuously with adaptive sampling intervals from 1 to 15 minutes. When traffic deviates outside its expected corridor — with seasonality-aware baselines — an anomaly is flagged.
The AI layer receives full context: live order data, historical patterns, enrichment data, error classification. Returns a verdict, a likely cause, and a confidence level.
A structured alert reaches your channel — Telegram, email, or Slack
— with the exact checks to run. One click opens the dashboard
pre-filtered to the incident.
The right alert
to the right person
Every time
automatically — so your ops team, analysts, and account
managers each see only what they need to act on.
— so your ops team, analysts, and account managers each see only what they need to act on.
Feature Highlights
See where orders fail to become transactions
— before the acquirer is involved. The traffic you're losing
before a transaction even starts, made visible. Key metrics: Conversion to Tx and Unknown Rate.
See where orders fail to become transactions — before the acquirer is involved. The traffic you're losing
before a transaction even starts, made visible. Key metrics: Conversion to Tx and Unknown Rate.
Not just a flag — a verdict.
Verdict, Observed data, Likely cause, Confidence level,
Checks to run. Structured every time.
Not just a flag — a verdict. Verdict, Observed data, Likely cause, Confidence level, Checks to run. Structured every time.
System incidents, provider degradations, and business anomalies go to separate channels — so the right people get the right alerts. Ops, analysts, and support each see what they need.
System incidents, provider degradations, and business anomalies go to separate channels — so the right people get the right alerts. Ops, analysts, and support each see what they need.
Z-Score Anomaly Detection, Moving Average Analysis, Bayesian Beta-Binomial Modeling,
and Wilson Score Confidence Interval Validation. Seasonality-aware baselines mean
night dips don't trigger false alerts.
Z-Score Anomaly Detection, Moving Average Analysis, Bayesian Beta-Binomial Modeling, and Wilson Score Confidence Interval Validation. Seasonality-aware baselines mean night dips don't trigger false alerts.
Built into your gateway.
Ready when you are
Request a demo and we'll show you what it looks like on your traffic.
Contact your account manager to enable the module.
