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AWS Bug Prevention CI Guardrail

3.00

Derivation Chain

Step 1 Proliferation of AWS-based AI bug prevention automation
→
Step 2 Demand for AI quality gates in CI/CD pipelines
→
Step 3 Automation of regulatory evidence and audits for AI code verification results

Problem

Domestic IT service companies in the financial and healthcare sectors (with 10-50 developers) are adopting AI-based bug prevention tools (e.g., Autonoma) into their CI/CD pipelines, but they fail to systematically record the 'quality evidence of code verified by AI tools' required by supervisory authorities (Financial Supervisory Service, Ministry of Food and Drug Safety). As a result, they spend 40-80 hours per audit manually reconstructing logs during compliance audits.

Solution

It integrates as a plugin into CI/CD pipelines (GitHub Actions, Jenkins, etc.) to automatically collect AI code verification results, coverage, and exceptions, and archive them in audit evidence formats (PDF/JSON). It provides templates for each regulatory framework (e.g., Financial Electronic Banking Supervision Regulations, Medical Device Software Validation).

Target: DevOps/QA teams at financial and healthcare IT service companies, with 10-50 developers, subject to regulatory audits.
Revenue Model: SaaS monthly subscription at 390,000 KRW (approx. $292.50) per team (up to 5,000 builds per month), Enterprise at 990,000 KRW (approx. $742.50) per month (unlimited builds + custom audit templates + dedicated support).
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
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 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.
N=.15 U=.20 M=.15 R=.30 V=.20

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/ 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 (56/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
12.0/15
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

Backend [medium] Infrastructure [medium] Frontend [low]
Dashboard