Public Institution Hiring Fairness Auditor

2.70

Derivation Chain

Step 1 KORAIL large-scale Recruitment

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Step 2 Public institution NCS-based Recruitment

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Step 3 NCS Recruitment process operations

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Step 4 Recruitment process fairness verification tools

Problem

When public institutions like KORAIL conduct large-scale Recruitment, they repeatedly face audit investigations over hiring corruption allegations. HR managers must retroactively prove fairness across the entire process — document screening, written exams, and interviews — but manually verifying interviewer assignment patterns, score distribution outliers, and evaluation criteria consistency takes 2-4 weeks per Recruitment cycle, costing over $7,500 in labor.

Solution

Upload data from the entire Recruitment process (document screening scores, written exam results, interview evaluation forms), and the platform automatically detects score distribution anomalies by interviewer, performs statistical correlation analysis between applicant attributes (alma mater, region, gender) and acceptance rates, calculates evaluation criteria consistency scores, and generates fairness certification reports aligned with Board of Audit and Inspection standards.

Target: Public institution HR teams (100+ employees, 50+ hires/year), public institution audit offices, Recruitment outsourcing consultancies (ages 30-50, HR professionals)

Revenue Model: Per Transaction $742/Recruitment cycle analysis, Annual Subscription $7,200 (4+ Recruitment cycles/year), audit response consulting referral commission 15%

Ecosystem Role: Regulation

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

3.0/5

M Market

2.0/5

R Realizability

2.0/5

V Validation

3.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (70%)

Tech Complexity

29.3/40

Data Availability

20.6/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (54/100)

Competition

8.0/20

Market Demand

9.4/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

7.5/15

Solo Buildability

5.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

Backend [medium] Frontend [medium] AI/ML [low]

Dashboard