As AI infrastructure expands and manufacturing power demand grows, KEPCO's industrial rate plans are becoming increasingly complex, yet SMEs with annual revenue of $3.75M-$37.5M (~5-50억원) lack electricity contract specialists and use suboptimal rate plans. Simply by switching rate plans, improving power factor, and managing peak loads, annual savings of $2,250-$11,250 (~300-1,500만원) are achievable — but external energy consulting costs $1,500-$3,750 (~200-500만원) per engagement, which is prohibitive.
A self-service tool where users upload their KEPCO electricity bill PDF to receive: (1) optimal rate plan simulation compared to their current plan, (2) auto-generated recommendations for power factor and peak hour improvements, (3) monthly savings impact Reports. Differentiated by a simulation engine that accurately reflects KEPCO's rate structure (Class A/B/C contract types).
| 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. |
| 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. |
| 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. |