End-to-end hiring automation for a moving company: license OCR, scoring, knockouts, insurance loop, and SMS/email reminders.
A regional moving company runs hiring through Indeed → Jotform → manual review → insurance check → onboarding. Each candidate eats 30+ minutes of owner time, weak candidates make it deep into the pipeline, and good ones get lost during the insurance wait.
Four chained Make scenarios. The main pipeline sends the candidate's driver's license photo + answers to Claude vision, gets back extracted fields and 1-10 scores in four categories, then routes: knockouts → auto-reject, low scores → auto-reject, qualified drivers → insurance loop. A nudge scenario watches the PendingInsurance store and sends timed candidate updates plus owner escalations. A booking watcher fires four reminders (email + SMS) at 48h / 24h / 2h / 30min.
Owner time per candidate drops to near-zero on rejected paths and ~5 minutes on qualified paths (only the insurance forward). All candidate-facing messaging is on-time and consistent. Human approval gate retained at the final insurance step.
Claude vision is given the license photo + a strict JSON schema. The downstream router doesn't care that it's an AI — it sees fields and scores. Adding a new score category is a prompt edit + a router branch, not a model swap.
Hard disqualifiers (can't work Saturdays, can't pass drug test) are checked first, so we don't waste a vision call on a candidate who's already out. Saves API spend and tightens the rejection email turnaround.
Make has no native cross-scenario queue, so the PendingInsurance datastore acts as the seam. Main pipeline writes; nudge pipeline (hourly) reads and acts; insurance loop clears the row. Each scenario is independently restartable.
Insurance clearance is the one step that's not safe to automate — wrong call here is a legal liability. Owner forwards the insurance reply through a tiny private Jotform to mark the candidate cleared; everything downstream is automatic again.
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