B

Career Expertise Demand Map

3.10

Derivation Chain

Step 1 Experience-based re-employment and solo entrepreneurship matching
→
Step 2 Lack of market demand visibility for career expertise
→
Step 3 Mapping demand sources by industry and role for experienced professionals

Competitor references

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

Problem

When professionals in their 50s with 25+ years of industry experience seek to leverage their expertise after retirement, job platforms like WorkNet or Saramin lack filters for '15+ years experience' and salary floor constraints make searches impossible. Meanwhile, SMEs and startups desperately need advisory from corporate veterans but have no way to connect. This information asymmetry has left both sides unmatched for years.

Solution

Users input their industry, role, and core competency keywords, and the tool visualizes demand for their expertise in a map format across four tabs: (1) SME advisory demand, (2) government expert pool recruitment, (3) university/public institution mentoring, and (4) solo consulting opportunities. Each tab crawls and displays recent postings and projects in real time.

Target: Ages 50–60, retired 1–3 years from large corporations or public institutions, 20+ years in manufacturing, IT, finance, or construction
Revenue Model: Free basic demand map access. Custom demand-matching report at $7.40/month (~9,900 KRW). 10% brokerage commission on successful advisory project placements.
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
2.0/5
V Validation
4.0/5
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 (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 (61/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
16.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
12.0/15
Solo Buildability
5.0/10
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

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