AI Biz Course Comparison Bot

3.60

Derivation Chain

Step 1 Explosion of AI Education courses such as AI Business Master Classes

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Step 2 Course selection confusion among Education consumers

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Step 3 Curation chatbot that compares and recommends AI business Education courses

Problem

Mid-career managers and business owners (ages 30–50) looking to adopt AI in their work spend an average of 5–10 hours comparison-shopping among the flood of AI business courses (50+ new offerings per month) from providers like Sookmyung Women's University, Fast Campus, and Inflearn to find one matching their industry, role, and goals. Cases of wasting 300,000–1,000,000 KRW (~$225–$750) in tuition on mismatched courses are frequent.

Solution

Auto-crawl domestic AI business courses to build a normalized DB of curricula, instructors, student reviews, and pricing + chatbot interface that recommends the top 3–5 optimal courses based on user input (industry/role/budget/Learning goals) + summarized student review insights.

Target: Corporate mid-level managers (ages 30–50), Small Business Owners, and Self-employed professionals in non-technical roles looking to adopt AI in their work

Revenue Model: Basic Free (5 recommendations/month) + Premium 19,000 KRW (~$14)/month (unlimited recommendations + in-depth review analysis), CPA fee of 30,000–50,000 KRW (~$22–$37) Per Transaction from Education institutions upon enrollment conversion

Ecosystem Role: Consumer

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

2.0/5

U Urgency

3.0/5

M Market

4.0/5

R Realizability

5.0/5

V Validation

3.0/5

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.

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

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 (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	/ 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 [low] Data Pipeline [medium]

Dashboard