B
Transit Authority AI Performance Reporter
2.65
Derivation Chain
Step 1
Seoul Metro AI Transformation - 28 Projects
→
Step 2
Public Agency AI Project Performance Management
→
Step 3
Multi-Project AI Performance Dashboard
Problem
IT department heads at public agencies managing 10-30 concurrent AI projects spend 25-30 hours per month consolidating progress rates, budget execution rates, and KPI achievement rates from spreadsheets and official correspondence. Non-standardized reporting formats across projects require an additional 10 hours to compile unified status reports for executive leadership, and failure to detect underperforming projects early results in a 40% year-end performance shortfall rate.
Solution
Collects progress, budget, and KPI data from each AI project via standardized forms, displays them on a unified dashboard, automatically identifies and flags underperforming projects, and auto-generates monthly reports for executive leadership and auditors. Also analyzes cross-project duplicate investments and synergy opportunities.
NUMR-V Scores
NUMR-V Scoring System
| 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. |
SaaS N=.15 U=.20 M=.15 R=.30 V=.20
Senior N=.25 U=.25 M=.05 R=.30 V=.15
Feasibility (70%)
Data Availability
20.6/25
Feasibility Breakdown
| 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. |
Market Validation (52/100)
Validation Breakdown
| 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. |
Technical Requirements
Backend [medium]
Frontend [medium]
AI/ML [low]