B

AI Competition Submission Validator

2.65

Derivation Chain

Step 1 Expansion of university AI competitions
→
Step 2 AI competition management platforms
→
Step 3 Automated submission verification & plagiarism detection service

Competitor references

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

Problem

When universities and public institutions host AI competitions, judges must manually review each team's submissions (code + model + presentation materials) for plagiarism, executability, and performance reproducibility. For 50 teams, first-round screening requires 3 judges spending 8 hours each — 24 person-hours (approximately $2,700 in labor costs) — and an average of 30% of submissions fail to reproduce, resulting in significant time waste.

Solution

Automatically executes submitted code in a sandbox environment to verify performance metrics reproducibility, detects plagiarism via AST-based code similarity analysis, and delivers results through a judge-facing dashboard. Key differentiators: templates tailored for Korean university competitions (pre-configured for Google Cloud and Naver Cloud environments) and AI-powered summarization of Korean-language presentation materials.

Target: Industry-academia cooperation offices at four-year universities hosting 1+ AI competitions per year (approximately 120 nationwide) and AI project departments at public institutions
Revenue Model: Per-competition Billing: $370 for up to 50 teams, $670 for up to 100 teams, $1,120 for up to 200 teams. Annual Subscription at 3x event price for unlimited competitions.
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
2.0/5
M Market
2.0/5
R Realizability
3.0/5
V Validation
3.0/5
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 (69%)

Tech Complexity
29.3/40
Data Availability
19.4/25
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 (51/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
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 [low] AI/ML [medium]
Dashboard