A

R&D Tax Credit Data Cost Documenter

4.00

Derivation Chain

Step 1 AI training data costs included in R&D tax credits
→
Step 2 Tax credit applications by AI Startups
→
Step 3 Automated data purchase documentation and classification tool

Competitor references

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

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 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 (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/ 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] AI/ML [low]
Dashboard