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Small restaurants and catering businesses (revenue $75K–$375K) must manually rebuild their menus every time seasonal ingredients change. Checking ingredient prices, balancing nutrition, and recalculating costs separately takes an average of 8–12 hours per menu rotation. When seasonal trends like 'spring greens bibimbap' explode on social media, failing to update menus quickly means missed revenue opportunities.
Combines agricultural distribution price data (KAMIS) with weather bureau API data to automatically recommend the most cost-effective seasonal ingredients at any given time, then auto-generates meal plans, recipes, and cost sheets using those ingredients. An SNS trend alert feature for trending ingredients helps operators time their menu updates.
| 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. |