ZANUS AI FOR FIELD SERVICE & REPAIR — AUTOMATIONS
Describe the job in plain words — “when a job closes, draft the service report from the tech’s notes and queue the customer summary” — and it becomes connected blocks on a canvas, running on its own, with every run visible.
Private tenant or your own server · unlimited technicians · your manuals never train a public model

A real flow: job-closed trigger, notes and manual sources, AI compile, outputs queued — drawn from a description.
Job closed → the AI reads the tech’s notes and photos, drafts the service report in your template and the plain-language customer summary, and queues both for review.
New OEM bulletin arrives by email → filed to the right library, affected models identified against your install base, a digest drafted for Monday’s toolbox talk.
Day-before → confirmation in the customer’s language; morning-of → the on-the-way notice. Rescheduling requests flow back to the calendar, not to a sticky note.
Run states live on the flow: green success, red failure on the exact block. A silently broken reminder flow means a tech at a locked gate at 8 AM — here, nothing fails silently.

Your rules: queue everything for human approval, or auto-send only the categories you designate as safe — reminders yes, repair recommendations never.
← Previous: Team Training · Sharing with a colleague? 📄 Get the PDF · ✉️ Email this page ·