A mid-market logistics-ops business brought Aivid's Audit Agent in on week one. By week three, a dispatch-scheduling AI employee was live and the operations team was working fewer hours on a faster cycle.
The customer operated four distribution centres. Each shift, dispatch coordinators reconciled driver availability, live order volume, and equipment readiness by hand — bouncing between a shared spreadsheet, an inbox, and back-and-forth phone calls with drivers.
Cycle times were inconsistent, recovery slips were invisible until they hit a customer, and the ops team had no shared view of which steps actually took the time. They had no baseline for measuring any automation effort either.
Week one was the Audit Agent: it catalogued 18 repeatable processes across the operations function and scored each on a 0–100 automation opportunity scale. The dispatch-to-dispatch cycle surfaced as the highest scorer.
Week three, a dispatch-scheduling AI employee was stood up against that top-scoring process — reading live order volume, driver availability, and equipment readiness; surfacing the next dispatch decision for a human reviewer. The agent reports daily into the customer's ops inbox.
In the first 90 days, dispatch-to-dispatch cycle time dropped 62% and the ops team reclaimed 11 hours per week previously lost to manual reconciliation. Annualised labor savings are being measured; we will publish a verified figure once the customer's finance team signs off.
The customer will be named and quoted in this story after measurement is complete. Today, the numbers stand on the Audit Agent's process model and the dispatcher's daily digest.
“We stopped reconciling dispatch by hand on the third week, and the ops team noticed the hours within the first month. The dashboard tells us what's real — it isn't a sales pitch.”