B

SME Senior Talent Board

2.70

Derivation Chain

Step 1 Experience-based re-employment matching
→
Step 2 Limitations of seniority-based filters on existing platforms
→
Step 3 Decomposing 'industry-specific know-how' needed by SMEs into skill-unit matching

Competitor references

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

Problem

An owner of an SME with 50 or fewer employees wants to hire a 30-year veteran manufacturing quality control expert, but filtering for '20+ years experience' on job portals like Saramin or JobKorea only surfaces high-salary candidates from large corporations. What they actually need is a micro-skill match like 'ISO 9001 audit experience + small factory line design.' Headhunters charge 20-30% of annual salary as fees, which is prohibitive for SMEs.

Solution

Senior job seekers register their careers decomposed into 'industry × job function × specific skill tags,' and SMEs search and match by 'required skill tags' on a web board. Since matching is skill-based rather than salary-based, part-time, advisory, and project-based contracts are naturally facilitated.

Target: Ages 50-60, retirees or soon-to-retire workers from large/mid-size corporations, 20+ years of experience in a specific industry, preferring advisory/part-time roles over full-time re-employment
Revenue Model: Free registration for job seekers. Per-match connection fee of 300,000 KRW (~$225) for employers (1/10 of headhunter fees). Monthly Subscription (unlimited search) at 190,000 KRW/month (~$142).
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
2.0/5
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.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (68%)

Tech Complexity
29.3/40
Data Availability
18.3/25
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 (50/100)

Competition
8.0/20
Market Demand
3.8/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
9.0/15
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] Backend [medium] Data Pipeline [low]
Dashboard