B

HRD Career-Matched Roadmap

3.30

Derivation Chain

Step 1 Mid-career Learning and certification roadmap
→
Step 2 Unified search across fragmented training programs
→
Step 3 Optimal certification and Education path design based on career history

Problem

Pre-retirees aged 50-60 looking to earn certifications for re-employment or starting a business face 100,000+ courses on HRD-Net, 3,000+ on K-MOOC, and thousands more at local lifelong learning centers — but no service tells them 'with 30 years of manufacturing quality control experience, which certification leads to income fastest.' They end up spending 6-12 months on inefficient certifications, wasting $1,500-3,750 (~200-500만원) in tuition and opportunity costs.

Solution

Users input their previous role, industry, and years of experience. The system auto-searches HRD-Net, Q-Net (certifications), and K-MOOC for relevant courses and generates a step-by-step roadmap like 'With your background → Certification A (3 months, government-funded, 72% employment rate) → Course B (online, Free) → start Freelancer work in Field C.' It also shows real-world examples of where people with each certification actually work.

Target: Workers aged 50-60, 1-3 years before retirement or recently retired, 20+ years in technical or office roles, preparing for re-employment, entrepreneurship, or Freelancer transition
Revenue Model: Basic roadmap generation Free (1 time). Customized detailed roadmap (training provider comparison, government funding application guide) at $4.40/report (~5,900원). Training institution referral commission
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
3.0/5
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.
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/ 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 (53/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
7.0/10
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

Data pipeline [medium] Backend [medium] Frontend [low]
Dashboard