AI Vendor SLA Violation Evidence Collector

3.30

Derivation Chain

Step 1 Anthropic's government ouster + rapid growth of Chinese AI causing vendor instability

→

Step 2 Increasing frequency of AI vendor service outages and SLA violations

→

Step 3 Automation of evidence collection and compensation claims for SLA violations

Problem

Korean B2B SaaS companies (10-50 employees) using AI APIs for core features often give up or underclaim compensation when AI vendors experience service outages, response delays, or quality degradation, because they fail to systematically collect evidence of SLA violations. Since SLA terms vary by vendor and logs at the time of incidents are difficult to restore later, they frequently miss out on credit compensation worth 1-5 million KRW (approx. $750-$3,750) per incident.

Solution

Monitor AI vendor API response times, error rates, and quality metrics 24/7, and automatically archive evidence (timestamps, error logs, response time graphs) immediately upon detecting SLA violations. Register SLA terms for each vendor to provide one-stop service: automatic classification of violation types, estimation of claimable compensation, and automatic generation of claim templates.

Target: DevOps/infrastructure engineers at Korean B2B SaaS companies with high AI API dependency (10-50 employees)

Revenue Model: SaaS monthly subscription: 99,000 KRW (approx. $74) per month for monitoring 3 vendors. Pro: 199,000 KRW (approx. $149) per month for unlimited vendors + automatic claim generation. Success fee: 10% of compensation credits obtained.

Ecosystem Role: Regulation

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

5.0/5

U Urgency

3.0/5

M Market

3.0/5

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.

N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (69%)

Tech Complexity

29.3/40

Data Availability

20.0/25

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 (53/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

7.5/15

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

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

Dashboard