From Resume Parsing to Explainable Candidate Ranking
The full journey a resume takes — parsing, confidence scoring, embedding, hybrid ranking — and why the 'why' matters as much as the score.
A resume enters as a messy PDF. It should leave as a ranked, explained recommendation. Here's the full journey.
1. Parse with confidence
The Resume Intelligence Engine extracts employers, titles, skills and dates — and attaches a confidence score to every field. Low-confidence values are flagged for human review instead of silently trusted.
2. Embed
Parsed profiles are embedded into a vector space alongside role requirements, enabling semantic matching that keyword search can't do.
3. Rank — and explain
The final score blends semantic similarity with structured signals. Crucially, it returns a rationale: why this candidate ranked where they did.
Why explainability is non-negotiable
Recruiters, hiring managers and auditors all need to trust the ranking. A black-box score erodes trust; an explained one builds it. That's the whole point of the system.
This is a placeholder draft — the full article is coming soon.