B

Retirement Pension Academy MatchBot

2.80

Derivation Chain

Step 1 Retirement Pension system changes and growing academy demand (Korea Investment & Securities)
→
Step 2 Corporate Retirement Pension training demand
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Step 3 Retirement Pension training instructor-company matching Platform

Problem

With Korea's 2026 mandatory default option for Retirement Pension plans and accelerated DB-to-DC conversions, HR and general affairs staff at SMEs with 50–300 employees must conduct annual legally mandated Retirement Pension training. However, free seminars from securities firms are mostly sales pitches for their own products, finding independent instructors relies on personal connections, and comparing lecture fees and instructor expertise is difficult — taking an average of 2–3 weeks to find a suitable match.

Solution

The service compares profiles, reviews, and fees of Retirement Pension specialist instructors (certified labor attorneys, pension actuaries, financial education lecturers) and uses AI to recommend instructors matched to company size, industry, and conversion type (DB/DC/IRP). It handles scheduling, online/offline selection, and automatic issuance of legally required training completion certificates as PDF — all in one stop.

Target: HR and general affairs staff at SMEs with 50–300 employees, companies preparing for DB-to-DC Retirement Pension conversion
Revenue Model: 15% Platform commission on lecture fees. Free for companies; instructor-side Premium profile placement at $29/month. Commission reduced to 10% for annual training contracts.
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
3.0/5
V Validation
2.0/5
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 (72%)

Tech Complexity
29.3/40
Data Availability
23.1/25
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 (50/100)

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
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

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