AI Training Course Enterprise Custom Matching Engine

3.20

Derivation Chain

Step 1 AI education popularization such as CMU's open AI courses

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Step 2 Surge in demand for AI training for corporate employees

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Step 3 Automated matching and comparison service for AI training courses by job role

Problem

As strengthening AI capabilities becomes a survival task for companies, HR managers need to select AI training programs for employees, but with over 500 domestic and international AI training courses available, finding the right course for each job role consumes 20-30 hours per HR person per month. Comparing curricula between courses is difficult, and there is no data on post-completion practical application rates, making it impossible to calculate training ROI.

Solution

By inputting the company's job composition and AI capability goals, it automatically crawls domestic and international AI training courses and recommends the optimal course for each job role. (1) Job-training course matching algorithm (based on curriculum topic analysis), (2) Dashboard comparing price, duration, difficulty, and completion rate across courses, (3) Automated post-completion practical application tracking surveys and ROI reports.

Target: HR and education teams at mid-sized companies with 50-500 employees, and traditional manufacturing and financial companies pursuing AI transformation.

Revenue Model: SaaS monthly subscription: 50,000 KRW (~$37.50) per HR account (up to 200 employees), Enterprise: 150,000 KRW (~$112.50) per month (unlimited + API), education institution listing sponsor: 500,000 KRW (~$375) per month.

Ecosystem Role: Infrastructure

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

2.0/5

U Urgency

3.0/5

M Market

4.0/5

R Realizability

3.0/5

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 (72%)

Tech Complexity

29.3/40

Data Availability

22.5/25

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 (60/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

12.0/15

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

Dashboard