An always-on AI pipeline over a live ConnectWise Manage PSA at a US managed service provider. Every ticket event fires a callback into the automation platform, the pipeline decides whether that event is worth an inference, and only then runs structured extraction over the ticket text, its notes and its schedule entries.

What it produces is a ten-field record per ticket: a written summary, the customer's sentiment, and typed lists of impacted software, hardware, IT services, facilities, permissions granted and sensitive data touched. A second pipeline runs three times a day, finds the tickets where the customer sounds unhappy, and pushes a Teams Adaptive Card with an Acknowledge button behind it, so the same ticket is never raised twice.

The engineering that matters is the gate. The pipeline recorded 2,097,878 successful executions between November 2025 and September 2026. Change detection against the stored row skips the model entirely when nothing material moved, and the workflow branches early on action and on service board, so a large share of events exit before inference is reached. On a stream this size that gate is the difference between a viable system and an unaffordable one. Batching, retry with backoff, a self-managing Postgres schema and a degradation path that records an empty result rather than failing the row sit behind it.

The sentiment loop is the part people touch: 761 notification runs, answered 108 times by the service desk, with acknowledgement checked before the model call so handled tickets cost nothing.

Honest limits: the 2.1 million counts callback events the pipeline handled, not AI summaries produced, and the same window carries about 1.2 percent failed executions. Built and operated solo. The client environment is not named and no client data appears here.