B

Human-Centered AI Welfare Benefit Matching

3.05

Derivation Chain

Step 1 LGU+ human-centered AI vision
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Step 2 The problem of people in their 50s missing out on welfare benefits they are eligible for
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Step 3 Lack of tools for institutions providing welfare benefit guidance (welfare centers, community centers) to automate personalized guidance.

Problem

Staff at local welfare centers and community service centers who need to guide residents aged 50+ to tailored welfare benefits (employment subsidies, health checkups, education costs, housing support, etc.) cannot keep track of the more than 200 programs from central ministries, local governments, and public institutions that change frequently. As a result, residents miss 30-40% of benefits they are eligible for, and staff spend over 10 hours per week on inquiries.

Solution

(1) Welfare staff input basic resident information (age, income bracket, household type, health status), (2) automatically extract a list of matching welfare programs and sort by priority, (3) automatically send personalized notices to residents via KakaoTalk/SMS. Differentiation: actual integration with social welfare public APIs (national pension enrollment, welfare facility information, etc.) + staff workflow automation tool.

Target: Welfare officers at local welfare centers and community service centers (aged 30-50), with jurisdiction over 5,000+ residents aged 50+, and institutions overloaded with welfare program guidance tasks.
Revenue Model: Free: basic matching (50 cases/month). SaaS: 50,000 KRW (~$37.5) per month: unlimited matching + automatic Kakao notification sending + matching statistics report. B2G annual contract: 2,000,000 KRW (~$1,500) per year for municipal-level adoption.
Ecosystem Role: Supplier
MVP Estimate: 1_month

NUMR-V Scores

N Novelty
2.0/5
U Urgency
4.0/5
M Market
4.0/5
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 (58%)

Tech Complexity
24.7/40
Data Availability
20.8/25
MVP Timeline
12.0/20
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 (57/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
9.0/15
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

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