B

Celebrity Appearance Risk Pre-screening

3.40

Derivation Chain

Step 1 Rapid rise and fall of broadcast talent popularity (e.g., Yang Sang-guk, Seol Woon-do)
→
Step 2 Demand for talent risk management among broadcasting and advertising production companies
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Step 3 Automated screening service for talent reputation risk

Competitor references

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

Problem

When small advertising agencies (5-30 employees) and broadcasting production companies cast models or talent, manually searching for past controversies, legal issues, and social media sentiment takes 2-4 hours per case. If a controversy erupts after contract signing due to missed verification, it can lead to disposal of all advertising materials (30-100 million KRW, approx. $22,500-$75,000 in losses) or broadcast editing costs. This is especially frequent in the trot and variety show sectors, where there is a need to quickly verify individuals whose popularity suddenly surges.

Solution

Entering a talent's name automatically crawls news, communities, social media, and court precedents to provide a comprehensive report on past controversy history, current public sentiment, and legal risks. Real-time monitoring during the contract period sends immediate alerts when new issues arise, and calculates a risk rating (safe/caution/danger).

Target: Account executives at small advertising agencies with 5-30 employees, casting directors at broadcasting production companies, and brand marketing managers
Revenue Model: Per-transaction billing: 29,000 KRW (approx. $22) per verification report, monthly subscription of 99,000 KRW (approx. $74) per month (10 verifications + real-time monitoring for 3 people). Premium at 199,000 KRW (approx. $149) per month (unlimited verifications + monitoring for 10 people).
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
5.0/5
M Market
3.0/5
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.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (70%)

Tech Complexity
29.3/40
Data Availability
20.4/25
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 (60/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
12.0/15
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] AI/ML [medium] Frontend [low]
Dashboard