Shipbuilding Talent Matching Agent

2.55

Derivation Chain

Step 1 HD Hyundai Samho next-generation shipbuilding leader discovery

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Step 2 Worsening labor shortage in shipbuilding industry

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Step 3 Automated talent matching between shipyards and regional universities

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Step 4 Shipbuilding internship performance tracking & matching Platform

Problem

Korea's Big 3 shipbuilders (HD Hyundai, Hanwha Ocean, Samsung Heavy Industries) and their subcontractors must recruit thousands of interns and new hires annually, but internship agreements with regional universities (Mokpo Maritime University, Geoje College, etc.) are managed manually between individual contacts. Intern competency evaluation, placement optimization, and post-internship hiring conversion tracking are unstructured, resulting in top talent attrition rates exceeding 30% and an average of 4 hours of administrative work per match.

Solution

Provides a Platform that automatically matches shipyard job-specific staffing needs with students' skills and preferred roles. Tracks mentor evaluations, attendance rates, and technical test scores in real time during internship periods, and auto-generates hiring conversion recommendation Reports upon internship completion. Reduces HR team administrative burden by 80% for shipyards, while universities gain real-time employment statistics.

Target: HR teams at shipbuilders and subcontractors with 100B+ KRW (~$75M+) revenue; career services offices at maritime/shipbuilding regional universities

Revenue Model: B2B SaaS monthly flat rate — 390,000 KRW (~$292)/site for shipyards, free for universities (two-sided market). 500,000 KRW (~$375) per successful hiring conversion as a performance fee

Ecosystem Role: Education

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

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

20.8/25

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

6.2/20

Timing

16.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

3.0/10

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