AI Agent Training Simulator

3.85

Derivation Chain

Step 1 Proliferation of general-purpose AI agents (Perplexity Computer, etc.)

→

Step 2 Non-developer demand for AI agent usage training

→

Step 3 Sandbox training Platform for safely practicing AI agent usage

Problem

Korean companies with 30–100 employees looking to adopt AI agents (Perplexity Computer, OpenClaw, etc.) find that non-developer staff (marketing, sales, HR) take an average of 2–4 weeks to learn how to use agents. Practicing on live business systems leads to 1–2 incidents per month, such as accidental data modifications or sending incorrect emails.

Solution

Provides a simulator where employees can practice using AI agents in a sandbox that mirrors the real work environment. Offers role-specific mission scenarios (marketing, sales, HR) and simulates the outcomes of agent commands for safe Learning. Automatically evaluates each learner's agent proficiency and provides a progress dashboard for team managers.

Target: HR/Education managers or digital transformation leaders at companies with 30–100 employees planning to adopt AI agents

Revenue Model: SaaS monthly flat rate of 39,000 KRW (~$29)/account (learner), team package of 10 accounts for 290,000 KRW (~$218)/month. Custom enterprise scenario creation: 1,000,000–2,000,000 KRW (~$750–$1,500) Per Transaction

Ecosystem Role: Education

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

4.0/5

M Market

4.0/5

R Realizability

4.0/5

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.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (74%)

Tech Complexity

29.3/40

Data Availability

24.4/25

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 (55/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

9.0/15

Pick-Axe Fit

9.0/15

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