B

Pre-Retirement Pay Stub Analyzer

3.55

Derivation Chain

Step 1 Career transition matching
→
Step 2 Poor understanding of pre-retirement pay components
→
Step 3 Pay stub item-by-item post-retirement change simulation

Problem

Office workers at large corporations approaching age 55 cannot accurately determine how each line item on their pay stub (base salary, position allowance, meal stipend, vehicle maintenance, performance bonus, welfare benefits) will disappear or convert after retirement. They don't know which items count toward the average wage used to calculate severance, or how tax-exempt items affect retirement income tax—leading to severance payouts frequently coming in 2–5 million KRW (~$1,500–$3,750) lower than expected. Getting clarity requires asking HR or consulting a labor attorney at 100,000–200,000 KRW (~$75–$150) per session.

Solution

Upload a pay stub PDF on the web or manually enter line items, and the tool visually breaks down each item's inclusion in severance calculation, retirement income tax impact, and post-retirement status (eliminated or converted). It generates an estimated net severance payout along with an auto-generated 'pre-retirement pay item checklist.'

Target: Ages 52–58 office workers at large/mid-size corporations, 1–3 years before retirement, with 20+ years of tenure
Revenue Model: Basic analysis report free; detailed severance simulation + PDF report download at 5,000 KRW (~$3.75) per report; Monthly Subscription for pay change tracking at 9,900 KRW (~$7.50)
Ecosystem Role: Supplier
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
4.0/5
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.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (70%)

Tech Complexity
29.3/40
Data Availability
20.6/25
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 (51/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
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

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