B

Physical AI Lab Equipment Booking Hub

3.15

Derivation Chain

Step 1 Expansion of physical AI testing labs (regional industrial AI transformation)
→
Step 2 Testing lab operations infrastructure

Problem

Physical AI testing labs are being established at universities and local governments nationwide, but booking and usage management for expensive equipment — robotic arms, sensors, GPU servers — is handled via Excel spreadsheets and KakaoTalk messages. Equipment idle rates exceed 40%, while booking conflicts are frequent during peak hours. Equipment failure history and consumable replacement cycles go untracked, causing delays in testing experiments.

Solution

Provides a lab equipment calendar booking system, equipment utilization and failure history dashboard, and automated consumable reorder alerts. Builds a cross-university equipment sharing reservation network to maximize utilization of idle equipment.

Target: National and private university AI testing lab operations managers, local government industry-academia cooperation center management teams (3-10 staff)
Revenue Model: SaaS Monthly Subscription $150/month per lab (~199,000 KRW, up to 20 devices), 5% commission per shared booking transaction
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
3.0/5
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 (69%)

Tech Complexity
24.0/40
Data Availability
25.0/25
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 (59/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
10.5/15
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]
Dashboard