B

AI Grad Portfolio Match

2.65

Derivation Chain

Step 1 Surge in AI talent graduates
→
Step 2 AI talent Recruitment market
→
Step 3 AI talent-company matching tools

Problem

AI-specialized universities like GIST produce 300+ AI graduates annually, but their project portfolios are scattered across GitHub, papers, and presentations. Recruiters spend an average of 2+ hours per candidate evaluating skills. As AI talent demand surges, small-to-mid IT companies face information asymmetry that puts them at a disadvantage against large corporations in talent acquisition.

Solution

AI graduates and junior developers link their GitHub, papers, and presentations, and AI automatically analyzes tech stacks, project complexity, and paper contributions to generate standardized competency profiles, then auto-matches them to company job descriptions. Core features: (1) GitHub/arXiv auto-analysis-based competency scoring, (2) Automated JD-candidate matching with fit scores, (3) One-page portfolio auto-generation.

Target: HR managers at IT/AI Startups and mid-size SI companies with 10–100 employees, aged 30s–40s
Revenue Model: Employer SaaS ₩190,000/month (~$143) (50 matches/month) + Candidate Premium ₩9,900/month (~$7.40) (portfolio enhancement feedback, employer view notifications)
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
3.0/5
V Validation
2.0/5
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 (70%)

Tech Complexity
29.3/40
Data Availability
20.8/25
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 (50/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
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] AI/ML [medium] Frontend [low]
Dashboard