Pre-Retirement Welfare Settlement Tool

4.50

Derivation Chain

Step 1 AI Builder Survival Guide — Career Transition for Employees

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Step 2 Administrative omissions in retirement preparation for those in their 50s

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Step 3 The problem of leaving without using up company welfare, points, and benefits before retirement

Problem

Employees aged 52-58 at large corporations and mid-sized companies must individually check unused annual leave allowances, company welfare points, tuition support, remaining health checkup items, group insurance conversion rights, and employee stock selling timing at the time of retirement. Even when contacting the HR team, different staff handle different items, taking an average of 3-5 days, and missed items average 500,000 to 2,000,000 KRW (approximately $375 to $1,500).

Solution

Automatically generates a checklist of company welfare items to check, use up, or convert at D-90, D-60, D-30, and D-7 before retirement. Based on a standard welfare item database by company size (large corporation, mid-sized, public institution), when users check their company's applicable items, it shows each item's deadline, responsible department, and required documents on a timeline.

Target: Employees aged 52-58 at large corporations, mid-sized companies, and public institutions, in the planning stage 6 months to 1 year before retirement.

Revenue Model: Basic checklist free; customized timeline + document template package 9,900 KRW (approximately $7.50). For corporate HR teams, a retirement guidance SaaS at 100,000 KRW per month (approximately $75).

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

5.0/5

M Market

3.0/5

R Realizability

5.0/5

V Validation

4.0/5

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.

N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (76%)

Tech Complexity

32.0/40

Data Availability

24.4/25

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 (59/100)

Competition

8.0/20

Market Demand

9.4/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

7.0/10

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 [low] Frontend [medium]

Dashboard