AI agent that evaluates job applications against your custom scorecard, ranks candidates, and flags top matches for manual review. Reduces screening time for small hiring teams.
A hiring screen agent automates the first pass of candidate evaluation by scoring applications against criteria you define—skills, experience, culture fit, availability. Instead of manually reading dozens of resumes, you set up a scorecard (e.g., "5 years Python experience = 10 points, degree required = 5 points"), feed in applications, and get ranked results. For small teams without dedicated recruiters, this agent handles volume filtering. It reads cover letters, resumes, and application form responses; scores each candidate; and surfaces the top 10–20% for your actual interview. The agent doesn't hire—it screens. Key operator benefit: you control the scorecard. If your hiring priorities shift (suddenly need DevOps experience over frontend), you update the criteria and re-run. The agent learns nothing; it applies your rules consistently. Setup requires: (1) defining your scorecard in plain language or structured format, (2) choosing how applications arrive (email, form, ATS export, API), (3) selecting an LLM backbone (GPT-4, Claude, open-source), (4) testing on 5–10 real applications to tune scoring weights. Most small teams complete this in 2–4 hours.
Privacy notes
Applications are sent to your chosen LLM provider (e.g., OpenAI, Anthropic) for scoring. Review that provider's data retention and privacy policy. Consider using a private or self-hosted LLM if handling sensitive candidate data. Do not store scorecard logic in the LLM; keep it in your config.