A

AI Deployment Power Cost Estimator

4.05

Derivation Chain

Step 1 Large-scale AI chip contracts → surging AI infrastructure power demand
→
Step 2 AI infrastructure power management
→
Step 3 Pre-deployment power cost estimation for AI-adopting companies

Problem

IT infrastructure teams at mid-sized companies (50-200 employees) planning on-premise AI server deployments cannot accurately estimate the power cost increase from GPU server operations, frequently exceeding budget by 30-50% post-deployment. Korea Electric Power Corporation's industrial rate structure (base charges, demand charges, time-of-use differentials) is complex, requiring external consulting at ~$2,300-$3,800 for accurate simulation.

Solution

Users input planned GPU server specs (model, quantity, utilization rate) and existing power contract details, and the system automatically calculates monthly/annual power cost increases based on KEPCO industrial rate structures. Provides time-of-use operation schedule optimization, cooling load estimation, and power contract change (standard/optional) simulation.

Target: IT infrastructure managers at mid-sized companies (50-200 employees) evaluating on-premise AI server deployment; data center operators
Revenue Model: Per Transaction billing: basic estimate ~$38, detailed simulation (with optimization) ~$113. Annual Subscription ~$75/month (3 reports/month + rate change alerts)
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
5.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
4.0/5
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 (76%)

Tech Complexity
34.7/40
Data Availability
20.8/25
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 (53/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
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

Backend [medium] Frontend [low] Data Pipeline [low]
Dashboard