B

AI Content Disclosure Guideline Trainer

3.15

Derivation Chain

Step 1 Surge in value of human-made work amid AI flood
→
Step 2 Trend of mandatory AI usage disclosure
→
Step 3 Onboarding service to educate and embed AI usage disclosure standards in organizations

Problem

When marketing, media, and education companies with 20–200 employees want to adopt 'AI usage disclosure guidelines,' industry-specific regulations and platform policies (Google, YouTube, Naver) vary, so establishing internal standards takes 2–3 months, plus an additional month for company-wide training. Missing disclosures risks platform penalties and loss of customer trust.

Solution

Select your industry and platforms, and the system automatically generates a customized AI usage disclosure guideline document and provides an interactive quiz-based onboarding module for employees. It automatically reflects regulatory and platform policy changes monthly, updates the guidelines, and sends retraining notifications.

Target: Compliance and HR managers at marketing agencies, media startups, and edtech companies with 20–200 employees
Revenue Model: SaaS monthly subscription: 49,000 KRW/month per organization (approx. $37, up to 50 employees); large: 99,000 KRW/month (approx. $74, up to 200 employees); 20% discount for annual billing.
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
4.0/5
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 (73%)

Tech Complexity
29.3/40
Data Availability
23.3/25
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 (56/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
8.0/10
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

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