B

Municipal Welfare Budget Education Kit

2.65

Derivation Chain

Step 1 Public subsidy populism controversy
→
Step 2 Insufficient fiscal literacy among municipal council members & officials
→
Step 3 Auto-generated fiscal impact analysis Education content

Problem

A significant number of local council members and newly assigned welfare officers lack fiscal analysis capabilities, relying solely on political judgment rather than fiscal sustainability reviews when drafting welfare budgets. Existing Education consists of 1-2 annual in-person training sessions, which cannot address the surge in pledge review demand during election seasons.

Solution

Auto-generates simulation-based Education content using actual municipal fiscal data (from Jaejung365). Provides an interactive simulator where users experience scenarios like 'What happens to our municipality's fiscal independence ratio and debt ratio if we add a welfare program costing $X million?' along with a case study bank comparing examples from other municipalities.

Target: Municipal council members (3,000+ nationwide), newly assigned welfare and finance officers at metropolitan and municipal governments (5,000+ new assignments annually)
Revenue Model: B2G Annual Subscription ~$3,600/municipality (4.8 million KRW, unlimited access for council members + officials). Commissioned training for council member academies and civil servant Education centers at ~$1,500 per session (2 million KRW).
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
2.0/5
M Market
2.0/5
R Realizability
3.0/5
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 (71%)

Tech Complexity
29.3/40
Data Availability
21.2/25
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
9.4/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
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

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