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When SMEs in IT (10-50 employees) developing and operating AI services need to create an AI-BOM (AI Bill of Materials), they spend an average of 40-80 hours per project manually tracking model, dataset, and library versions, licenses, and sources. With regulations tightening—EU AI Act, Korea's AI Basic Act, etc.—the risk of delivery rejection or penalties for incomplete BOMs is becoming a reality.
Automatically scans Python/JS project dependency trees to collect model cards, dataset cards, and library licenses, then auto-generates standard AI-BOM formats (SPDX, CycloneDX). Provides a change history tracking dashboard and regulation-specific compliance checklists, reducing audit response time by 90%.
| 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. |