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

Competitor references

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

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