임대차 전자계약·문서 자동화 도구
공인중개사 매물-고객 자동매칭 SaaS
수작업 매칭에 병목을 겪는 중개사 30만+명
Real estate agents manually and repeatedly enter property photos, descriptions, and spec sheets across platforms like Naver Real Estate, Zigbang, and Dabang. Among 300,000+ agents, many spend over 30 minutes per listing on registration, and the pressure to transition to e-contracts is adding to the administrative burden.
When property photos are uploaded, AI extracts specs such as area, direction, and floor level, and automatically generates descriptions that meet each platform's guidelines. Input: photos + basic information; Output: platform-optimized listing description sets and one-click registration links.
| 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. |