Recruitment enrichment engine
Set the role, skills, location and experience once. The pipeline finds matching profiles, adds whatever public signal exists, scores each one against the job and hands the recruiter the top 40% with the reasoning.
- Candidates a day
- 500+
- Reviewed by a person
- the top 40%
- Screening time
- down about 90%, client reported
- Platform
- Make.com
Why it exists
Recruiters spend most of the day on search, copy paste and first pass screening. Almost none of that needs judgment. It's repetitive data work.
How it runs
Define
Role title, required skills, location and experience level, set once per opening.
Scrape
Apify searches LinkedIn, GitHub and job boards for matching candidates and returns structured profiles.
Enrich
GitHub activity, portfolio links, publications and any other public signal get added.
Score
An AI model rates each profile against the job requirements and writes down its reasoning. The bottom 60% are dropped.
Store
Candidates above the line go into Airtable with their score, key strengths and suggested interview questions.
Notify
The recruiter gets a Slack message or email: how many found, how many qualified, the top three.
What came out of it
Recruiters only look at the top 40%, each with a score, strengths and questions to ask. The client reported screening time down about 90% and a 32% lift in candidates converting to interview.
Notes
This one runs on Make.com and OpenAI rather than n8n and Claude because that's what the client already had. The pattern is the same. Scraping profiles has terms of service limits, so volume is kept under what the sources tolerate.
Stack
- Make.com
- Apify
- OpenAI API
- Airtable
- Slack
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