B

University Faculty Hiring Audit Tool

2.80

Derivation Chain

Step 1 Kangwon National University faculty hiring scandal
→
Step 2 University faculty Recruitment transparency improvement

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

National university faculty hiring processes repeatedly experience procedural violations including unmet consent requirements, reviewer conflicts of interest, and missing documentation. Issues like the Kangwon National University case only surface after media coverage, and full audits of past hiring cases take an average of 5 days per case and 3-6 months overall.

Solution

Digitizes the faculty hiring process from job posting to final appointment using checklist-based tracking, flagging potential violations against the Educational Public Officials Act and university regulations in real time. Provides automated conflict-of-interest detection for reviewers (co-authored papers, same alma mater, etc.), automatic quorum verification for consent votes, and automated audit trail preservation.

Target: National university HR and academic affairs offices, private university audit departments (institutions with 100+ faculty members)
Revenue Model: SaaS Annual Subscription at 2,400,000 KRW/year (~$1,800/year) (unlimited hiring cases), automated audit Report generation module add-on at 600,000 KRW/year (~$450/year)
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
3.0/5
V Validation
2.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (69%)

Tech Complexity
29.3/40
Data Availability
20.0/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (51/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
9.0/15
Solo Buildability
6.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

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