B

AI Litigation Evidence Collector

3.35

Derivation Chain

Step 1 Rise in AI copyright lawsuits
→
Step 2 AI litigation specialized Legal services
→
Step 3 Automated AI litigation evidence collection and preservation tool

Problem

AI-related copyright disputes are surging, as seen in the three major Korean broadcasters' lawsuit against OpenAI. When small law firms (3-10 attorneys) take on AI copyright cases, they must collect and preserve evidence that AI service outputs are similar to original copyrighted works. However, AI service outputs vary by time of query, and simple webpage screenshots lack sufficient legal evidentiary weight. Evidence collection and preservation takes 20-40 hours per case.

Solution

An automation tool that inputs specific prompts into AI services and preserves output results with timestamps and hash values via blockchain anchoring. Generates one-click legal evidence packages with similarity analysis reports comparing outputs to original works. Includes recurring monitoring to track output changes over time.

Target: Small law firms with 3-10 attorneys and Freelancer attorneys handling AI/IT copyright disputes
Revenue Model: Per Transaction billing: 150,000 KRW (~$112) per evidence collection (prompt set + preservation + Report). Monthly Subscription at 290,000 KRW (~$217)/month (5 cases included, additional cases at 100,000 KRW / ~$75 each)
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
4.0/5
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
20.0/25
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 (55/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
9.0/15
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] AI/ML [medium] Frontend [low]
Dashboard