B

Disaster Revenue Compensation Proof Assistant

2.90

Derivation Chain

Step 1 Frequent nationwide heavy snow and cold waves
→
Step 2 Small business owners applying for natural disaster damage support
→
Step 3 Automatic generation of disaster damage sales decline evidence documents

Problem

Small business owners affected by natural disasters like heavy snow or typhoons must prepare 5-8 types of evidence, including pre/post-disaster sales comparison tables, damage photos, and insurance documents, to apply for government disaster relief funds. Those lacking tax knowledge spend 3-5 days on paperwork or pay 300,000-500,000 KRW (approx. $225-$375) to tax accountants, and the initial rejection rate due to incomplete documents reaches 40%.

Solution

It integrates card sales (Credit Finance Association), POS data, and National Tax Service HomeTax sales reports to automatically generate pre/post-disaster sales comparison tables, and matches with Korea Meteorological Administration alert data to automatically create damage causation statements. It outputs PDFs tailored to each local government's disaster relief application form with one click.

Target: Self-employed restaurant and retail business owners with annual sales of 100 million to 1 billion KRW (approx. $75,000-$750,000), especially small business owners in weather-disaster-prone regions (Gangwon, Chungcheong, Gyeongbuk)
Revenue Model: Per-transaction billing: 19,000 KRW (approx. $14) per application; annual subscription at 9,000 KRW (approx. $7) per month (unlimited applications); 10% referral fee when connecting with tax accountants
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 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 (76%)

Tech Complexity
34.7/40
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 (56/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
10.5/15
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

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