Youth Data Purge Assistant

2.90

Derivation Chain

Step 1 OS-level age verification mandate

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Step 2 Strengthened minor personal data protection

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Step 3 Legacy collected data cleanup service

Problem

With mandatory age verification and strengthened COPPA/privacy laws, companies now face obligations to identify and delete minor data previously collected without age confirmation. For SME app services, manually identifying suspected minor data in databases requires 2–4 weeks of developer time, and failure to comply risks fines of hundreds of thousands of KRW (~hundreds of USD) per violation.

Solution

Connects to enterprise databases to automatically identify suspected minor accounts through behavioral patterns (sign-up time of day, content consumption patterns, name/email patterns) and generates deletion/anonymization target lists. Preserves deletion history as audit logs and auto-generates regulatory compliance documentation.

Target: Chief Privacy Officers (CPOs) and backend developers at Korean social/education/gaming app startups with 10–30 employees operating services with 10K–500K monthly active users

Revenue Model: Initial scan at 290,000 KRW (~$217) Per Transaction + monthly monitoring SaaS at 49,000 KRW (~$37)/month per service, with additional Billing for database scale (10,000 KRW (~$7.50) per 100K records beyond 1M records)

Ecosystem Role: Regulation

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

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 Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (68%)

Tech Complexity

29.3/40

Data Availability

18.8/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (56/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

5.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

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

Dashboard