A

AI Training Data Provenance Chain

3.85

Derivation Chain

Step 1 AI unauthorized news training lawsuits
→
Step 2 Growing demand for AI model training data transparency
→
Step 3 Training data provenance tracking Infrastructure
→
Step 4 Automated provenance certification issuance service

Problem

With the EU AI Act taking effect (August 2026), general-purpose AI model developers must publicly disclose a training data summary. Korean AI startups entering overseas markets need to document the source, license, and collection timestamp for thousands of datasets to meet this requirement, but there is no standard format and no clear verification method.

Solution

Provides an SDK (Python/Node.js) that automatically records source metadata (URL, license, collection timestamp, hash value) at the point of dataset collection: (1) auto-generates training data provenance summaries in EU AI Act format, (2) tracks dataset change history (git-like version control), (3) issues provenance certification certificates (verifiable hash chain) for third-party verification.

Target: Korean AI startups preparing for EU market entry (5-30 employees), system integrators handling AI model development outsourcing, AI governance consultants
Revenue Model: SDK Free (open source) + Cloud SaaS: Basic at ~$143/mo (manage 500 datasets + summary generation), Pro at ~$370/mo (unlimited + certificate issuance + audit logs), certificate issuance at ~$38 Per Transaction
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
5.0/5
U Urgency
4.0/5
M Market
4.0/5
R Realizability
3.0/5
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 (78%)

Tech Complexity
34.7/40
Data Availability
23.1/25
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 (60/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
18.0/20
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 [low] Frontend [low]
Dashboard