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SME pharma companies (50-150 employees) with AI in manufacturing manually compile audit trails—AI model input/output logs, drift monitoring results, retraining history—for regular GMP inspections, requiring 2-3 weeks and 1-2 dedicated staff per inspection. Missing trails result in corrective action orders or AI usage suspension, dropping process efficiency by 15-20%.
Auto-collect AI model inference logs, drift metrics, and retraining history to continuously generate audit documents in GMP audit format. Pre-inspection auto pre-check warns of missing items in advance, and a read-only portal is provided for auditors to review.
| 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. |