R&D Tax Credit Data Cost Documenter

4.00

Derivation Chain

Step 1 AI training data costs included in R&D tax credits

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Step 2 Tax credit applications by AI Startups

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Step 3 Automated data purchase documentation and classification tool

Problem

AI Startups and SMEs can now include AI training data purchase costs in their R&D tax credit claims, but documenting each data purchase as 'AI training purpose' and establishing its connection to R&D activities is complex. Outsourcing to a tax accountant adds 500K-1M KRW (~$375-$750) per case, while handling it internally consumes 1-2 weeks per quarter for the person in charge, with frequent missed deductions.

Solution

Users upload data purchase receipts and contracts, and an LLM automatically determines whether each qualifies as 'AI training data' and generates supporting documentation formatted for R&D tax credit applications. The system structures dataset purpose, project association, and amounts into quarterly summary reports and provides advance warnings when deduction limits are about to be exceeded.

Target: Finance/admin managers at AI Startups and SMEs with 5-30 employees, and tax accountants serving these companies

Revenue Model: SaaS at 59K KRW/month per account (~$44/month, includes 50 documents); additional documents at 1,000 KRW each (~$0.75). Pro plan for tax accountants at 190K KRW/month (~$142/month, unlimited clients)

Ecosystem Role: Infrastructure

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

4.0/5

M Market

4.0/5

R Realizability

4.0/5

V Validation

4.0/5

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 (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	/ 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 (60/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

12.0/15

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

Backend [medium] Frontend [low] AI/ML [low]

Dashboard