Combine service telemetry, deployment activity, and affected transactions in a shared incident view. Xect AI helps teams assess probable causes, estimate transaction exposure, and coordinate approved response actions with verification of service recovery.
A checkout failure shows up as latency in one tool, errors in another, abandoned carts in a third, and a release in the deployment log eleven minutes earlier. Nobody sees all four signals together until the bridge call. Xect AI brings service telemetry, including Dynatrace and Kibana, deployment activity and Commerce transaction data into one incident view, scores the anomaly against a learned per-endpoint baseline in MLflow, estimates the affected order value, and routes the proposed rollback through an approval gate with the nearest prior incident attached.
Error rates by endpoint, latency percentiles, failed carts and orders, deployment events, traffic by country and B2B unit, Solr query health and downstream response times arrive in the Business Operators Hub as versioned events, at the rate the platform already ingests.
CheckoutHealth@champion scores each endpoint’s failure rate and latency against its learned normal, seasonality included; IncidentSimilarity@champion matches the signature against resolved incidents. The anomaly at 10:42 is a model decision, not a threshold crossing.
Four correlation branches run at once: share of failures per endpoint, time since the last deployment, concentration by country and business unit, downstream service latency. An impacted-order-value query prices the incident. The Incident Agent writes the card: probable cause, impact, similar case, suggested action.
The incident record opens in Jira or ServiceNow, the on-call is paged, the card lands in Slack or Teams. The rollback or timeout change waits for approval, runs through the CCv2 deployment API, and is verified against the same metric that raised the alarm.
Detection runs as a model, correlation as a flow, and the incident write-up as an agent. Each step hands the next its evidence. The incident owner reviews the probable cause, the impacted order value and the proposed action before anything executes.
Learns a baseline for each monitored endpoint, tuned to its own time-of-day and seasonal pattern
Owns the streaming scoring flow against CheckoutHealth@champion. Because the baseline is learned per endpoint with seasonality, a Monday-morning traffic peak stays quiet and a 4% payment failure rate at 10:42 does not. It opens the incident and hands over the anomaly window.
Correlates, prices and explains in one card
Runs the correlation branches in parallel, queries impacted order value for the anomaly window, pulls the nearest resolved incident from IncidentSimilarity@champion and writes the card: 82% of failures on /checkout/payment, started 11 minutes after deployment 20-3, primarily Germany B2B, about $1.3M in order value, probable cause downstream payment timeout, similar to INC-2287.
Files, pages, waits for approval, verifies
Opens the incident record, pages the on-call, posts the card with the proposed rollback or timeout change, and holds the action at the Human in the Loop gate. On approval it executes through the deployment API and watches the same failure-rate metric until it returns to its baseline band; the incident owner confirms recovery and closes the record.
From 0.4% to 4.7% on /checkout/payment. A static threshold would have fired on the previous peak and missed this one; the learned baseline fired here.
Every anomaly is timed against the last deployments automatically, as a signal for the reviewer, not proof of cause. “Eleven minutes after 20-3” is computed, not remembered on the bridge call.
The card shows the estimated order value inside the anomaly window and the segment carrying it. Estimated exposure provides commercial context for the response decision.
When the signature matches a resolved incident with a verified playbook, the proposed fix is ready the moment the on-call engineer opens the record, for a quick approval. New signatures still arrive ranked, priced and explained.
Black Friday load is not an incident and a 4% payment failure is. The baseline belongs to the endpoint.
This page covers commerce platform incidents on SAP Commerce Cloud. For order-level fallout from the same incidents, see Order Exception Management. Live Operations Map is a related pattern built for telecom network operations and is not commerce-specific.
A checkout failure on a replenishment portal costs order value by the minute. Impact is priced per B2B unit and country, so the rollback goes to the segment that pays for it.
See the industry pageAs a potential adaptation, the same correlation pattern extends to OSS, BSS and the self-service portal: an activation failure timed against the last release, priced in orders stalled.
See the industry pageAs a potential adaptation, quote and bind endpoints get their own learned baseline, keeping renewal-season load quiet while still raising a payment-gateway timeout in minutes.
See the industry pageAs a potential adaptation, booking-engine errors correlate with channel-manager deployments and property clusters, with the incident card naming the property group and the revenue at risk.
See the industry pageRun the same operations center across every client tenant, multi-tenant by default, with each client’s baselines, approvals and audit trail kept apart.
Talk to us about multi-tenant opsOwns availability and needs the anomaly, the deployment and the impacted value together, with the fix one approval away.
Owns the queue and needs the incident to arrive with the probable cause and the closest resolved case, not five console links.
Owns the release calendar and needs every anomaly timed against the deployments that preceded it.
Owns the revenue of the channel and needs the incident described in the currency of the business, not in error codes.
How CheckoutHealth@champion is called from a streaming flow and how its verdicts become actions.
Incidents and impact by hub and region, in real time. Built for telecom network operations; commerce incidents use a different scope here.
Verified playbooks that test, fix and confirm without a ticket. Built for telecom network operations; the playbook pattern applies here, the scope does not.
How rollbacks and configuration changes are gated, logged and rolled back.
Walk through a historical or simulated incident, the evidence supporting the response, and the required approval and recovery checks. A replay using your telemetry can be prepared after access and data requirements are agreed.