Xect AI models the expected behavior of each event stream, accounting for seasonal patterns such as weekends, holidays and month-end. It flags deviations that fixed-threshold monitoring typically misses, attaches supporting context, and routes them to an investigation or a defined response workflow. Retraining runs on schedule, and alert thresholds are tuned per stream rather than guaranteeing zero false positives.
AI EPM turns every registered event into a live dashboard; ML Anomaly Detection is the model that decides when that dashboard needs attention. Technical monitoring tells you a server is up. It typically can't tell you that Tuesday's self-install orders are half of what Tuesdays produce. Each model learns its own stream's expected range from the events already flowing through the platform and flags results only when actual values fall outside that range.
Monitors group by category — Account, Portal, Notifications, Orders, Technical Support — and by event: portal orders, disconnect orders, web orders, D2D orders, self-install, reactivations, suspensions, login success, successful payments, reschedules, password resets.
One model per stream: transactional integrity from raw technical logs, seasonality-aware forecasting for weekends, holidays and month-end.
Actual outside the modeled band, with confidence, triggers the alert with expected-vs-actual context attached; correlating the cause is a separate step, handled downstream. Inside the band, silence.
Scheduled retraining and drift checks keep the baseline accurate as the business grows; today-vs-last-week overlays show the shift.
Each agent owns one step in the stream-to-alert path above and hands off cleanly to the next.
Gives every event stream its own model
Auto-discovers streams from registered events, picks the features that matter for each one, and baselines against the event history already sitting in the Hub — a stream needs enough history before a baseline can be set.
Knows a Monday from a month-end
Models weekday, holiday and billing-cycle effects together with non-linear trends, then sets a tolerance band per hour instead of one flat threshold.
Detects deviations outside the expected range
Watches every stream against its own band, catching both high and low anomalies, and opens the matching flow the moment one crosses the line.
Tracks drift and keeps the model current
Runs scheduled retraining cycles, tracks drift and accuracy between them, and keeps every model version rollback-ready.
From portal orders to password resets, each stream is watched for its own kind of wrong.
The band is the model's, not a guess: a month-end surge is expected, a Tuesday dip is not.
Payment and order failures are flagged before the close, not after it.
Scheduled retraining alone doesn't guarantee accuracy. Drift and accuracy are checked between cycles so performance is reviewed, not assumed.
Traditional monitoring watches the servers. Xect AI watches whether the business is happening.
When self-install orders fall below Tuesday's band at 10 AM, Xect AI flags the portal defect before the day's D2D sales are lost.
See the industry pageWhen successful checkouts dip 30% against the forecast for a Saturday, the payment gateway is checked before the first complaint.
Talk to us about retailWhen login successes drop while attempts hold, the identity provider is flagged in minutes.
Talk to us about bankingWhen appointment reschedules spike outside the band for one clinic, the scheduling integration is checked that morning.
Talk to us about healthcareWhen scan events at one depot fall silent, the dock issue is found before the missed-delivery tickets arrive.
Talk to us about logisticsOwns the numbers the business runs on, and needs to know the moment a stream breaks pattern, not read about it after close.
Owns payment and order integrity, and needs the drop flagged before finance finds it at close, with the evidence already attached.
Owns the funnels customers move through, and needs a break in login, reset or order flow flagged before it becomes a support queue.
Owns the models behind every stream, and needs each one built automatically, retrained on schedule, and explainable when someone asks why.
Book a 30-minute demo and watch Xect AI model one of your event streams and flag deviations against your own history.