AI Model Performance Regression Watchdog

3.35

Derivation Chain

Step 1 Domestic GPU race / surge in AI Infrastructure investment

→

Step 2 Growing demand for AI model operations (MLOps)

→

Step 3 Automated performance degradation detection and alerting for models in production

Problem

ML engineers at Startups running AI services must manually monitor production model accuracy degradation (data drift, concept drift). Performance drops are discovered an average of 2–3 weeks late, during which customer churn increases 5–15% and emergency retraining costs 2–5 million KRW ($1,500–$3,750) in additional GPU expenses.

Solution

Connect model inference logs to detect input data distribution shifts (data drift) and prediction quality degradation in real time. Sends instant alerts via Slack/Discord and auto-generates reports on retraining necessity and estimated costs.

Target: MLOps/ML engineers at Series A–B Startups running AI services in production, aged 25–35

Revenue Model: SaaS Monthly Subscription: 1 model free, Pro at 49,000 KRW/month (~$37/month) (5 models + alerts + reports), Scale at 120,000 KRW/month (~$90/month) (20 models + dashboard + API)

Ecosystem Role: Infrastructure

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

4.0/5

M Market

4.0/5

R Realizability

3.0/5

V Validation

3.0/5

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 (69%)

Tech Complexity

29.3/40

Data Availability

19.4/25

MVP Timeline

20.0/20

API Bonus

0.0/15

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 (54/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

5.0/10

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

Data Pipeline [medium] Backend [medium] Frontend [low]

Dashboard