Big Tech Relocation Tax Savings Simulator

3.00

Derivation Chain

Step 1 Big tech HQ relocation / tax optimization trend

→

Step 2 Tax-saving-driven corporate relocation consulting

→

Step 3 Relocation scenario-based tax simulation tool

Problem

IT Startups and SMEs with annual revenue of $750K–$3.75M spend $375–$750 per consultation and 2–3 weeks when evaluating Tax savings through corporate headquarter relocation, comparing local tax reductions, Startup support zone benefits, and relocation costs. As Palantir-style HQ relocations gain attention in Korea, there is no tool to quickly simulate combinations of corporate tax, acquisition tax, and property tax reductions for regional relocation.

Solution

Builds a nationwide city/county/district-level corporate and local tax reduction database, and provides a simulator that auto-compares 5-year Tax savings across relocation candidates based on industry code, revenue, and headcount. Automatically factors in overlapping benefits from Startup support zones, innovation cities, and free economic zones, and visualizes the break-even point of net savings versus relocation costs (Real Estate, moving, employee attrition).

Target: CEOs/CFOs of IT Startups and small manufacturers with annual revenue of $375K–$3.75M, ages 30–50

Revenue Model: SaaS Monthly Subscription $37/mo per entity, 20% discount on annual billing. Tax Accounting expert referral commission of $37 per matched consultation

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

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 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 (63%)

Tech Complexity

24.0/40

Data Availability

18.8/25

MVP Timeline

20.0/20

API Bonus

0.0/15

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)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

7.0/10

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

Frontend [medium] Backend [medium] Data Pipeline [medium]

Dashboard