Hirebix

Scoring System

How Hirebix Scores Your Candidates

Every resume is evaluated through a rigorous, multi-dimensional AI analysis. No black boxes. Every score comes with evidence, reasoning, and full transparency.

2-Stage

Analysis pipeline

5 Sub-Scores

Per requirement

100%

Evidence-backed

Two-Stage Analysis

From Upload to Score in Minutes

Every resume passes through two AI analysis stages. The first checks structural fit. The second performs a deep, evidence-based evaluation.

Stage 1
Fit Analysis

Compares the candidate's structured profile data against your job requirements across five dimensions:

  • Skills alignment
  • Experience range
  • Education match
  • Location compatibility
  • Compensation fit

Uses structured data only. Fast and consistent.

Stage 2
Deep Screening

Reads the full resume text like an experienced hiring manager. Evaluates each requirement through 5 independent sub-scores:

  • Per-requirement sub-score breakdown
  • Evidence citations from the resume
  • Red flag detection with penalties
  • Weighted overall score computation
  • Confidence level assessment

Produces the final score with full reasoning trail.

Granular Evaluation

5 Sub-Scores Per Requirement

Each job requirement is broken into 5 independently scored dimensions. This mechanical approach prevents inflated scores and creates meaningful differentiation between candidates.

Skills Match

Exact Skill Match
Depth of Experience
Recency
Related Skills
Achievement Specificity

Years of Experience

Total Years vs Required
Relevance of Experience
Career Progression
Career Coherence
Impact Evidence

Education Level

Degree Relevance
Degree Level
Certifications
Continuing Education
Education-to-Role Alignment

Job Title Relevance

Current Title Alignment
Title Progression
Industry Alignment
Scope Match
Seniority Calibration

Score Breakdown

What the Screening Scores Mean

Candidates are scored 0-100 and placed into five clear tiers. Most candidates score between 30-60. A score above 65 means the candidate is genuinely strong.

80 - 100

~3% of candidates

Top Candidate

Exceptional fit. Clear evidence across all criteria with quantified impact.

Suggested action: Advance immediately

65 - 79

~12% of candidates

Great Fit

Strong evidence of relevant skills and experience. Minor gaps may exist.

Suggested action: Advance to next stage

55 - 64

~15% of candidates

Good Fit

Decent alignment with some gaps. Worth exploring if your pipeline needs depth.

Suggested action: Phone screen to probe gaps

36 - 54

~35% of candidates

Partial Fit

Significant gaps in key areas. Missing evidence for important requirements.

Suggested action: Likely decline

0 - 35

~35% of candidates

Low Fit

Missing key requirements. Little to no evidence of alignment with the role.

Suggested action: Decline

Profile Quality

Trust Score: How Reliable Is This Profile?

Before screening, every candidate profile is scored for completeness, verification, and data quality. This helps you know how much to trust the screening results.

35%

Completeness

How much of the profile is filled

40%

Verification

Cross-referenced with external sources

25%

Data Quality

Depth, consistency, and specificity

80 - 100ExcellentWell-documented profile with verified claims. External signals confirm resume data.
60 - 79ReadySolid profile. Most fields present with minimal discrepancies.
30 - 59Needs ReviewGaps in profile data or unverified claims. Manual review recommended.
0 - 29IncompleteSparse resume or significant verification failures.

Quality Control

Automatic Red Flag Detection

The AI actively looks for resume issues that experienced recruiters catch. Detected issues result in point deductions with specific evidence cited.

Keyword Stuffing

Skills listed without any context in work experience

Vague Claims

"Responsible for" or "involved in" with no measurable outcomes

Contradictions

Claims that conflict with timelines or other resume sections

Inflated Titles

Job title not supported by the described responsibilities

Copy-Paste Descriptions

Nearly identical bullet points across different employers

Fair & Equitable

Built to Reduce Bias, Not Amplify It

Our scoring system is designed to evaluate what candidates can do, not where they come from. Every design decision prioritizes substance over background.

No Name or Demographic Bias

Resumes are anonymized before scoring. No names, photos, or demographic data influence the evaluation.

No Institution Prestige Bias

Education is scored on relevance to the role, not school brand. Bootcamps, online degrees, and non-traditional paths are treated equally.

Substance Over Polish

Scoring evaluates what candidates achieved, not how eloquently they describe it. Non-native English speakers are not penalized.

Career Changers Welcome

Deliberate career transitions with retraining evidence are recognized. Transferable skills are counted at appropriate weight.

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