B
Pre-Retirement In-Company Benefits Checklist Service
3.80
Derivation Chain
Step 1
Increase in early retirement reviews due to AI fear
→
Step 2
Losses from unclaimed in-company benefits after deciding to retire
→
Step 3
Automatic generation of a timeline for in-company benefits to manage in the 6 months before retirement
Problem
Employees in their 50s who decide to retire have 10-15 in-company benefits to manage over the 6 months to 1 year before their actual retirement date (e.g., settlement of unused annual leave, tuition support deadlines, health checkups, changes to retirement pension investment instructions, timing for selling employee stock, repayment of company loans). However, there is no service that systematically guides them. Asking the HR team is complicated because different departments handle different items, and missing any can result in real losses of millions of KRW. In fact, it is estimated that retirees lose an average of 1.5 to 3 million KRW (approx. $1,125 to $2,250) due to unclaimed unused annual leave and neglected default options in retirement pensions.
Solution
By entering your expected retirement date and company type (large enterprise, mid-sized, public sector), it automatically generates a timeline from D-180 to D-Day of all in-company benefits to manage before retirement. It displays a checklist for each item (e.g., annual leave settlement formula, deadline for changing retirement pension investment instructions, health checkup deadline) and the estimated financial impact. Reminders are sent via KakaoTalk notifications based on D-day.
NUMR-V Scores
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 (71%)
Data Availability
21.7/25
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)
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
Frontend [medium]
Backend [medium]
Infrastructure [low]