A

AI Counseling Ethics Audit Auto-Scorer

4.20

Derivation Chain

Step 1 Expansion of youth AI mental health counseling pilots
→
Step 2 Demand for AI counseling service quality and safety management
→
Step 3 Automated ethics auditing for AI counseling conversation logs

Problem

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.

Solution

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.

Target: Local government mental health centers, education offices, and 10–50 person EdTech companies that have deployed AI counseling bots
Revenue Model: SaaS Monthly Subscription at $217/organization (~29만 원, up to 10K conversation logs/month), $0.015 (~20 원) Per Transaction for overages
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
5.0/5
M Market
4.0/5
R Realizability
4.0/5
V Validation
4.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (79%)

Tech Complexity
34.7/40
Data Availability
24.4/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (57/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
5.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

AI/ML [medium] Backend [low] Frontend [low]
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