Race Photo Auto Delivery

3.35

Derivation Chain

Step 1 Marathon running boom

→

Step 2 Demand for marathon race operation infrastructure

→

Step 3 Inefficiency in race photo capture and distribution

Problem

Marathon race organizers take thousands of photos on-site, but the process of delivering each participant's photos to them is manual. Sorting photos by bib number takes 2-3 photographers 3-5 days after the race, totaling 30-50 hours per year, and participants can't find their own photos to post on social media, reducing race experience satisfaction. Outsourcing photo sorting and distribution costs 1,000,000-3,000,000 KRW (approx. $750-$2,250) per race.

Solution

A B2B SaaS that automatically sorts race photos by participant using bib number OCR and face embedding matching, and sends personal gallery links to participants via KakaoTalk/SMS on the same day. Organizers only need to upload photos, and sorting is completed within 1 hour. Participants can download low-resolution photos for free, and purchase high-resolution or framed prints.

Target: Local government sports councils that host marathon or trail run events at least twice a year, running community operators, and sports event agencies (with 5-20 employees).

Revenue Model: Per-race billing: 300,000 KRW (approx. $225) for up to 500 participants, 500,000 KRW (approx. $375) for up to 1,000 participants, and an additional 500 KRW (approx. $0.38) per participant beyond 1,000. High-resolution downloads for participants are 2,000 KRW (approx. $1.50) per photo (revenue shared 50:50).

Ecosystem Role: Infrastructure

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

3.0/5

M Market

3.0/5

R Realizability

3.0/5

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.

N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (74%)

Tech Complexity

29.3/40

Data Availability

25.0/25

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 (74/100)

Competition

10.0/20

Market Demand

20.0/20

Timing

16.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

12.0/15

Solo Buildability

5.0/10

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] AI/ML [medium] Frontend [low]

Dashboard