B

Local Government SNS Legal Risk Monitoring

3.05

Derivation Chain

Step 1 Local revitalization and public official trends
→
Step 2 Expansion of local government SNS promotion
→
Step 3 Legal risk management for public officials' SNS activities
→
Step 4 Automation of pre-legal review of content

Problem

As local government SNS accounts proliferate, legal issues such as copyright infringement (unauthorized use of BGM and images), personal information exposure (unblurred citizen faces), and violations of the Public Official Election Act (content that could be misconstrued as promoting a specific candidate) occur 3-5 times per year per local government. Each incident incurs fines of 500,000 to 5,000,000 KRW ($375 to $3,750) plus 2-4 weeks for corrective action, and there is no staff for pre-review.

Solution

AI-based pre-review service before content upload. Detects copyright-risk materials (fonts, BGM, images) in images/videos, automatically blurs citizen faces, and detects expressions violating the Public Official Election Act and the State Public Officials Act. Issues a review completion certificate that can be used as evidence for post audits.

Target: Public relations teams and media centers of cities, counties, and districts nationwide (approximately 230 basic local governments)
Revenue Model: SaaS monthly subscription: 190,000 KRW per local government (approx. $143) (100 reviews per month). Additional 2,000 KRW per review (approx. $1.50). 20% discount for annual payment.
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
4.0/5
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 (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 (57/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
10.5/15
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

AI/ML [medium] Backend [medium] Frontend [low]
Dashboard