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When public utility power companies adopt AI-based Predictive Maintenance, they manually prepare performance reports for executives and auditors. Collecting and organizing per-equipment AI prediction accuracy, prevented failure counts, and cost savings from 12 separate systems takes over 40 hours per month, with frequent rework due to constantly changing report formats.
Automatically collect AI predictive maintenance logs from power plant SCADA/PI Historian systems, and visualize per-equipment prediction performance (true positive rates, early warning counts, estimated cost savings) in a standardized KPI dashboard. Auto-generate three report types—executive, auditor, and internal—with month-over-month trend comparisons.
| 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. |