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Over 50 AI mental health chatbots are now deployed by local governments and schools as of 2026, but ethical violations — such as missed self-harm/suicide risk signals, inappropriate advice, and excessive personal data collection — are reviewed manually after the fact. A single counselor spends 20+ hours per week reviewing over 200 conversation logs daily, and missed high-risk cases can lead to legal liability.
Upload AI counseling conversation logs to automatically score them for self-harm/suicide risk keyword detection, non-certified advice pattern identification, and excessive personal data collection, generating a risk-level report (red/yellow/green). Sends immediate escalation alerts to human counselors for high-risk conversations. Auto-generates monthly ethics audit reports for regulatory submission.
| 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. |