B

News Training Evidence Collector

3.15

Derivation Chain

Step 1 AI unauthorized news training lawsuits
→
Step 2 Media company copyright infringement evidence collection needs
→
Step 3 Automated detection service for news content AI training usage

Problem

When news media companies (broadcasters, major newspapers) try to verify whether their articles were included in LLM training data, they must run reverse-engineering prompt tests across thousands of articles against multiple AI models. Manual verification takes 15-20 minutes per article, and securing evidence for 100+ litigation cases requires Legal Affairs teams to work full-time for weeks.

Solution

A SaaS that takes article URLs or text as input, automatically sends prompts to 5 major LLMs (GPT, Claude, Gemini, LLaMA, HyperCLOVA) to test content reproduction, and packages similarity scores, response screenshots, and timestamps into Legal evidence format (PDF). Includes batch processing (bulk upload) and periodic monitoring (automatic re-testing when new models launch).

Target: Legal/copyright teams at news agencies with 50-500 employees, IP-specialized law firms handling litigation on behalf of media companies
Revenue Model: SaaS Monthly Subscription: Basic KRW 290K/month (~$218/month, 500 articles/month), Pro KRW 790K/month (~$593/month, 3,000 articles + auto-generated Legal evidence PDFs), law firm annual contracts quoted separately
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
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.
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/ 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 (57/100)

Competition
8.0/20
Market Demand
3.8/20
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] AI/ML [medium] Frontend [low]
Dashboard