A

AI R&D Expense Audit Assistant

3.90

Derivation Chain

Step 1 Launch of 763.2 billion won (~$572M) Science & Technology Innovation Fund
→
Step 2 Growing R&D budget management demand from expanded government S&T R&D spending
→
Step 3 Automated compliance and settlement tools for government R&D expense management at small research institutes

Problem

Small research institutes and startups conducting government R&D projects often fail to comply with National R&D Project Management Regulations during research fund expenditure, resulting in improper spending. During year-end settlement, they face clawbacks averaging $1,500-$3,750 (~2-5 million won) per case and potential sanctions restricting future contracts. Settlement paperwork takes 20-40 hours per project, and small organizations without dedicated staff spend $750-$1,500 (~1-2 million won) per project on outsourced accounting.

Solution

Connects to research fund expenditure records (card and transfer data) and performs real-time compliance checks per expense category, issuing immediate alerts for potential violations. Automatically generates year-end settlement documents (expenditure statements, supporting evidence attachments, balance summaries) and exports submission-ready files formatted to Ministry of Science and ICT settlement templates.

Target: R&D management officers at small research institutes and deep-tech startups conducting 2-10 government R&D projects
Revenue Model: SaaS Monthly Subscription: $44/month (~59,000 won) per project; $29/month (~39,000 won) per project for 3+ projects; automated settlement Report generation included
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
5.0/5
M Market
3.0/5
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.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (69%)

Tech Complexity
29.3/40
Data Availability
20.0/25
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 (59/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
12.0/15
Solo Buildability
5.0/10
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 [medium] Data Pipeline [low]
Dashboard