Office workers around age 55 continue paying fixed expenses such as monthly subscriptions, insurance, and tuition fees out of inertia. Free/low-cost alternatives like AI translation, AI tax, and AI legal services have emerged, but they keep paying for existing paid services (monthly translation subscriptions, tax agent fees, etc.), wasting 500,000-2,000,000 KRW (approx. $375-$1,500) annually. There is no systematic tool to check which expenses can be replaced with AI alternatives.
Users enter their current monthly fixed expense items on the web (or paste card statement text), and the service (1) matches each item against available AI/free alternatives, (2) shows estimated monthly/annual savings from switching, and (3) classifies replacement difficulty (immediate / learning required / not replaceable) to provide a prioritized action list.
| 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. |