AI-Assisted Hiring Screening

The business case

Hiring is expensive in professional services in a way that's easy to underestimate until you've done it wrong. A bad hire at the associate or manager level typically costs $50,000 - $150,000 when you add up recruiting fees, onboarding time, reduced team productivity during the ramp period, and separation costs. A large portion of wasted spend concentrates in the early screening stage, where someone is reading through 200 resumes and making judgment calls under time pressure, applying criteria that feel consistent but drift across a long candidate pool. This Play standardizes first-pass screening. Every applicant goes through the same evaluation criteria. The AI extracts the relevant signals, scores candidates against your defined profile, and delivers structured summaries to the hiring manager. The human makes every actual decision - the AI handles the reading, extracting, and organizing.

What this play does

When an application arrives through any intake channel, n8n receives it and passes the resume and application materials to the AI with your defined screening criteria. The AI extracts relevant information and scores the candidate against each criterion. A structured summary is generated: years of relevant experience, specific credentials, types of engagements handled, any flags relative to your criteria, and an overall fit score. Summaries are delivered to the hiring manager in a consistent format - all looking the same, in the same place, rather than a mix of PDF resumes and email attachments in different formats. The hiring manager works from the summaries to build their phone screen list.

Before and after

Business impact

A hiring manager who can process 200 applications in an hour instead of two days has recaptured significant capacity. For firms that run 6+ searches per year, the efficiency savings alone justify the time to build this Play. The quality improvement is harder to measure directly but matters more: a consistent screening process that applies the same criteria to every candidate reduces variability in who makes it to the phone screen, increasing the probability that the best candidates are in the room.

Prerequisites

Complete these before opening n8n. Skipping prerequisites is how you end up rebuilding workflows.

Step-by-step implementation

The steps below are the full build guide. Each step includes configuration notes and exact AI prompts where applicable.

You are a hiring specialist screening applications for a professional services firm. Your job is to read a resume and extract specific, verifiable information based on our screening criteria.

Resume text: {{resume_text}}
Applicant name (from application): {{applicant_name}}
Applicant email: {{applicant_email}}

Screening criteria to evaluate against:
{{screening_criteria}}

Extract information from the resume ONLY - do not infer or assume anything not stated in the resume text.

Return ONLY a valid JSON object with these fields:

{
  "applicant_name": "Name from resume (verify against application name)",
  "current_title": "Current or most recent job title",
  "current_company": "Current or most recent employer",
  "years_total_experience": number,
  "criteria_scores": {
    "[criterion_name]": {
      "score": number from 0 to 10,
      "evidence": "Direct quote or specific evidence from the resume supporting this score",
      "notes": "Any relevant context or caveats"
    }
  },
  "credentials_found": ["List of specific credentials, certifications, or licenses found"],
  "engagement_types": ["Types of work or engagements specifically mentioned"],
  "overall_fit_score": number from 0 to 100,
  "fit_tier": "strong_fit" (75+) | "potential_fit" (50-74) | "low_fit" (<50),
  "key_strengths": ["2-3 specific, evidence-based strengths relevant to the role"],
  "gaps_flags": ["Specific gaps relative to required criteria, or any flags (employment gaps >6 months, frequent short tenures, etc.)"],
  "data_confidence": "high" (clear evidence in resume) | "medium" (inferred from context) | "low" (sparse resume data)
}

Week-by-week rollout plan

Success benchmarks

These are the specific, measurable signals that confirm the play is working. Check against each benchmark at the 30-, 60-, and 90-day mark.

Common mistakes

Exception rule

Related plays