Public Data AI Preprocessing Pipeline

4.05

Derivation Chain

Step 1 Expanding public data AI utilization (Ministry of Interior survey)

→

Step 2 Growing demand for AI training data preprocessing

→

Step 3 Public data-specialized preprocessing automation pipeline

Problem

When AI Startups try to use public data for model training, each API has different response formats (XML/JSON/CSV), with Korean encoding errors, date format inconsistencies, and missing value handling required — data engineers spend an average of 2-3 days preprocessing per API. For teams using 10-20 APIs per month, 30-40% of annual data engineer labor costs are consumed by simple preprocessing.

Solution

Input a public data API URL to automatically collect responses, unify formats (JSON normalization), fix encoding, standardize dates, handle missing values, and remove duplicates with one click — outputting clean datasets ready for AI training. Preprocessing pipelines can be saved as templates for reuse in scheduled data collection.

Target: AI Startups and data analytics teams with 1-3 data engineers

Revenue Model: Per-call Billing: $0.375 per 1,000 calls, Monthly Subscription $36.75 (100K calls/month), 20% discount for annual billing

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

5.0/5

M Market

4.0/5

R Realizability

4.0/5

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.

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

20.6/25

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 (61/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

18.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

13.5/15

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] Frontend [low] Data Pipeline [medium]

Dashboard