B

AI Predictive Model Reproducibility Certification Center

3.30

Derivation Chain

Step 1 Development of AI future prediction models
→
Step 2 Commercialization of AI prediction models
→
Step 3 Reproducibility verification service for prediction model results

Problem

When AI startups and research institutions deliver 'future prediction' AI models to clients, they fail to prove output reproducibility for the same input, delaying contract signing. In the finance and insurance industries, regulatory bodies require evidence of model prediction consistency. Manual testing takes 2-3 hours per case, with 40-60 cases per month, resulting in approximately 1,500 hours of wasted labor annually.

Solution

Automatically records input-output pairs of AI prediction models, statistically analyzes result deviations when re-run under identical conditions, and automatically generates reproducibility certification reports. Provides version-specific performance drift detection, automatic PDF evidence formatting for regulatory submission, and audit history timeline visualization.

Target: MLOps engineers and CTOs at AI/ML startups with 5-30 employees, and companies delivering AI solutions to finance and insurance sectors.
Revenue Model: SaaS monthly subscription: 49,000 KRW per month per project (approx. $36.75), 20% discount for annual payment. Enterprise Plan: 190,000 KRW per month (approx. $142.50) with unlimited projects and dedicated audit reports.
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
4.0/5
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 (74%)

Tech Complexity
29.3/40
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 (67/100)

Competition
10.0/20
Market Demand
20.0/20
Timing
14.0/20
Revenue Signals
7.5/15
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

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