No usable source links are available for this idea. Its signal notes and scores need independent verification.
When AI Startups try to use public data for model training, each API has different response formats (XML/JSON/CSV), with Korean encoding errors, date format inconsistencies, and missing value handling required — data engineers spend an average of 2-3 days preprocessing per API. For teams using 10-20 APIs per month, 30-40% of annual data engineer labor costs are consumed by simple preprocessing.
Input a public data API URL to automatically collect responses, unify formats (JSON normalization), fix encoding, standardize dates, handle missing values, and remove duplicates with one click — outputting clean datasets ready for AI training. Preprocessing pipelines can be saved as templates for reuse in scheduled data collection.
| 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. |