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Dual-income couples in their 50s typically hold 5-8 policies each, totaling 10-16 policies combined, but checking for coverage overlaps requires opening each policy document individually across different insurer apps. It's common for overlapping coverage across medical expense insurance, cancer insurance, and whole life insurance to result in $150-300/month in unnecessary premiums, yet people leave them untouched because they lack the information to determine which policies would be a loss to cancel.
Upload each spouse's insurance policy PDFs or manually enter key coverage items (daily hospitalization allowance, surgical costs, cancer diagnosis benefit, medical expenses, etc.), and the system visualizes coverage overlaps in a matrix by coverage area. It compares savings from canceling overlapping policies vs. coverage gap risks, and provides a prioritized recommendation of which policies to cancel first.
| 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. |