B

Judicial Misconduct Sentencing Research Engine

3.75

Derivation Chain

Step 1 Judicial Misconduct Act passes National Assembly
→
Step 2 Case analysis tools for legal professionals
→
Step 3 Automated sentencing guideline research engine

Problem

Since the enactment of the Judicial Misconduct Act (법왜곡죄), attorneys and law firms must manually search for relevant precedents and sentencing guidelines to prove or defend against claims of judicial misconduct by judges and prosecutors. Searching for similar precedents takes an average of 4-8 hours per case, and since this is a newly established law, existing case databases lack proper classification systems, creating a high risk of missing relevant cases.

Solution

Automatically crawls and structures court ruling texts, tagging them by judicial misconduct elements (abuse of discretion, factual misapprehension, misinterpretation of law), and provides similar case search, sentencing deviation visualization, and defense argument draft generation.

Target: Criminal law attorneys with less than 5 years of practice, attorneys at small-to-mid-sized law firms with 5-20 staff
Revenue Model: SaaS Monthly Subscription at 99,000 KRW (~$74)/account, 20% discount for annual billing. Additional case Report at 19,000 KRW (~$14) Per Transaction
Ecosystem Role: Supplier
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
5.0/5
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 (70%)

Tech Complexity
29.3/40
Data Availability
20.4/25
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 (75/100)

Competition
10.0/20
Market Demand
19.4/20
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