AI Resume Parsing & Candidate Scoring

# AI Resume Parsing & Candidate Scoring ## Resume Parsing When a candidate uploads a resume (PDF, DOC, DOCX), HireBee's AI automatically extracts: - **Personal information** — name, email, phone, location - **Work experience** — companies, titles, dates, descriptions - **Education** — institutions, degrees, fields of study - **Skills** — technical and soft skills - **Languages** — spoken languages and proficiency - **Certifications** — professional certifications ### How It Works 1. Candidate uploads resume via application form or manual import. 2. AI processes the document and creates a structured profile. 3. Parsed data populates the candidate profile automatically. 4. Original file is preserved alongside the parsed data. ### Bulk Parsing Upload multiple resumes at once through the **Bulk Import** feature. Each file is processed in the background, and you're notified when complete. ## Candidate Scoring When a candidate applies to a job, AI evaluates their fit based on: - **Skills Match** — how well their skills align with job requirements. - **Experience Score** — relevance and duration of work experience. - **Education Score** — alignment with educational requirements. - **Culture Fit** — based on your organization's brand voice and values. - **Overall Score** — weighted composite of all factors (0–100). ### Score Breakdown Each score includes: - Numerical rating (0–100) - **Strengths** — what makes this candidate a good fit - **Weaknesses** — areas of concern - **Gaps** — missing qualifications - **AI Reasoning** — detailed explanation of the scoring logic - **Recommendation** — strong match, good match, partial match, or poor match ### Using Scores - Sort candidates by score in the pipeline view. - Set up auto-screening rules to automatically reject candidates below a threshold. - Use scores as a starting point, not a final decision — always review personally. ## Semantic Search Beyond keyword matching, HireBee uses AI embeddings for semantic candidate search. Search for concepts like "machine learning engineer with startup experience" and find relevant candidates even if those exact words aren't in their resume.