Screen candidates and send outreach in Make
A Make workflow parsed resumes, scored applicants, updated the ATS, and handled candidate messaging and recruiter handoff.
- What they did
- A Make.com scenario started when a new application arrived through an ATS webhook or a career-page webhook. It passed the resume URL to an AI parsing service, applied a weighted scoring rubric, then saved the score back into the ATS and sent an acknowledgment email to the candidate. Applicants above the cutoff were pushed to a recruiter in Slack with approve/reject buttons, and the same flow could also trigger Calendly scheduling, feedback reminders, and follow-up on candidates who never replied.
- How it went
- The setup did not report measured results, but it did say the score and ATS update could happen within minutes of submission. Recruiters received a ranked queue instead of a disorganized inbox. One limitation was that if the ATS lacked webhooks, the workflow had to poll every 15 minutes, and if it had no API, notification emails had to be parsed instead.
- Tips
- Keep the scoring rubric explicit so the weightings are consistent. Add a human approval step before any acknowledgment, rejection, or scheduling message is sent.
Try it yourself with Make
Set up a Make scenario for [job title] hiring. Watch [ATS, career page, or email inbox] for new applications, extract each resume with an AI parser, and score candidates using [criteria and weightings]. Write the applicant name, score, and a short reason into [ATS or tracking sheet], then draft an acknowledgment email and show it to me for approval before sending. For candidates above [threshold], send a shortlist to [Slack channel] with approve/reject buttons, and pause for approval before any interview invite, decline, reminder, or other message goes out. If the ATS has no webhook, poll its API every [interval]; if it has no API, watch its notification emails instead.
Fill in the [brackets]. Agents can make mistakes, so review anything before it is sent, paid or deleted.
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