A

AI Restructuring Severance Package Calculator

4.35

Derivation Chain

Step 1 AI Will Collapse Everything by 2028 — Wall Street Research Forecast
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Step 2 Support Services for AI Restructuring Targets
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Step 3 Automated Severance Package Fairness Verification & Negotiation Coaching

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

Employees targeted for restructuring due to AI adoption cannot determine whether the severance package offered by their company (severance pay, outplacement support, grace period, etc.) is fair. Most either consult a labor attorney (300,000–1,000,000 KRW / ~$225–$750 per session) or simply accept the offer as-is. Statistics show that accepting without negotiation typically results in severance pay 30–50% below the appropriate level.

Solution

A service where users input their severance package terms (severance pay, outplacement support duration, non-compete clauses, etc.) and receive a fairness score benchmarked against market data by industry, tenure, and job level, along with auto-generated negotiation talking points and conversation scripts for each package component.

Target: White-collar workers aged 30–50 who have received restructuring notices, with annual salaries of 40,000,000–80,000,000 KRW (~$30,000–$60,000)
Revenue Model: Basic fairness score free. Detailed analysis + negotiation script generation at 19,900 KRW (~$15) Per Transaction. Separate referral commission for labor attorney matching.
Ecosystem Role: Consumer
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
5.0/5
M Market
3.0/5
R Realizability
5.0/5
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.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (78%)

Tech Complexity
34.7/40
Data Availability
23.3/25
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 (56/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
8.0/10
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] Data Pipeline [low]
Dashboard