AI job-matching workflow

Parses candidate resumes against open roles, scores fit with reasoning, and routes ranked shortlists straight into the recruiter queue.

Human Resource · Stack: Claude, Make, Lovable. Outcome: Shortlisting effort per role down ~80%

Overview

A workflow that scores candidates against open roles with reasoning and drops ranked shortlists straight into the recruiter's queue.

The problem

Recruiters read every resume against every role by hand, so shortlisting is slow and the reasoning behind each pick is inconsistent.

How it works

• Parses incoming resumes into structured candidate profiles.
• Matches each profile against the open role's requirements.
• Scores fit with explicit reasoning for the rank.
• Routes the ranked shortlist into the recruiter queue.

What it does

• Resume parsing into structured profiles
• Role-by-role fit scoring
• Transparent reasoning for each match
• Auto-built recruiter shortlist

Architecture

Claude for parsing and scoring, orchestrated through Make, with a Lovable-built interface for recruiters.

Results

• Shortlisting effort per role down ~80%
• Consistent, explainable ranking
• Recruiters spend time on candidates, not triage