Product analytics ingestion

Qualetics collects usage and performance data from the products it is embedded in and turns it into analytics. A Beam agent reads incoming metric records and applies the customer rule to tag, group, or forward them to the right dashboard or downstream system. Processed records are written back with their status noted for later checks. When a data stream stops, a metric arrives in an unexpected shape, or values fall outside the agreed range, the agent pauses that feed and notifies the data owner instead of passing questionable numbers into the reports that people rely on to make decisions.

Model output monitoring

Qualetics surfaces the outputs and quality signals of AI features running inside a product. A Beam agent reads prediction records and accuracy indicators, then applies the approved rule to note drift, log a threshold breach, or open a follow-up item. Findings are recorded against the model so the trail is clear for whoever reviews it. When output quality drops below the agreed level, or a signal the rule has no entry for appears, the agent hands the case to a data scientist with the recent readings attached, keeping judgment about model behavior firmly with a person.

Embedded insight alerts

Qualetics raises alerts when embedded analytics detect a meaningful change in product behavior. A Beam agent reads the triggered alerts and applies the customer rule to acknowledge, group related ones, or notify the responsible team through the connected channel. Each action is written to the alert history for reference. Alerts that cross a severity the rule does not handle, or that cluster in a way suggesting a wider fault, are escalated to an engineer with context gathered from the related records, so a person decides how urgent the response should really be.