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
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.
NUMR-V Scores
NUMR-V Scoring System
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
SaaS N=.15 U=.20 M=.15 R=.30 V=.20
Senior N=.25 U=.25 M=.05 R=.30 V=.15
Feasibility (78%)
Data Availability
23.3/25
Feasibility Breakdown
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
Market Validation (56/100)
Validation Breakdown
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |
Technical Requirements
Backend [medium]
Frontend [low]
Data Pipeline [low]