Then the warehouse started running the operation.

The data was unified, but operations still ran on memory. Commitments made in meetings and Slack died there. Nobody knew which sales calls were actually being recorded. Executives asked for numbers instead of receiving them.

An AI operations layer on top of the warehouse. An AI triage desk reads meeting transcripts and Slack, extracts committed action items, posts them for one-click human approval, and files approved tasks into the task system. Tagging the bot in Slack files a task in about one second.

Call recordings flow in automatically, with coverage views that show exactly which reps record and which do not. A daily scorecard posts itself to Slack every afternoon, a weekly summary every Monday, and a live executive dashboard splits return on ad spend into cash collected versus contracted revenue. Error alerts and a daily health check watch the whole system.

A nightly paid-ads verdict report marks every ad keep, watch or turn off, with spend matching Meta to the cent. A freshness guard now holds every automated report until the previous day's data has fully landed.

934 call recordings ingested and coverage-scored, which surfaced that more than half of the sales calls that ran were never recorded (99 of 226), a people problem no one could prove before. Executive reporting now arrives unattended.

225 action items extracted from 135 meetings and Slack threads for about $8 of AI in total. The freshness guard caught reports running on partial data that had overstated cost per lead by 62%, and the past posts were corrected the same day.

When a production incident hit, it was diagnosed and resolved the same day, and the core pipeline came out 30x faster (638 seconds to 21).