Candidate Pre-Screening Interview

AI pre-collects candidate details to maximize matching accuracy with companies

Background and Challenges

Standardized questions do not sufficiently reveal candidates' strengths or individuality, leaving insufficient information for screening decisions, and application essays and similar materials have become increasingly uniform due to the rise of generative AI

Open-ended responses are superficial, making it difficult to assess actual skill levels

Candidates end up repeating the same information to recruiters multiple times, creating significant inefficiency

Interviewers end up repeating the same questions, reducing time for deeper discussion and candidate engagement

AS-IS (Conventional)

・Standardized, superficial question items

・Short, surface-level free-text responses

・Basic items are re-confirmed during interviews

TO-BE (After Implementation)

・Role-specific AI drills into experience

・Structured data on skills and preferences

・Immediate decisions possible via ATS integration

Operational Flow

1. Send an interview request to the candidate

2. AI drills into detailed experience using role-specific scenarios

3. Structure the responses and automatically sync them with the ATS

4. Interviewers review the deep-dive report and start the interview

Information You Can Obtain

・Skills and experience detail report

・Competency and preference tags

・Conditions and constraints list

Implementation Effects (KPI)

・Pre-hire recruitment-related information: improved

・Mismatch rate: decreased

・Recommendation lead time: shortened