No-Code Decision Tree Builder

3.35

Derivation Chain

Step 1 Spread of decision tree ML educational content

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Step 2 Demand for decision automation among non-developers

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Step 3 No-code tool that visualizes small business rules as decision trees

Problem

Small business owners in e-commerce and service industries with annual revenue of 100 million to 1 billion KRW ($75,000-$750,000) manually handle repetitive decisions such as customer inquiry classification, shipping cost calculation, and discount application. They manage these with Excel IF functions or notes, leading to errors, and training new employees takes an average of 30 minutes per case, wasting over 15 hours per month. Existing RPA/automation tools are too complex to set up and cost over 200,000 KRW ($150) per month, so they give up on adoption.

Solution

A no-code tool that allows users to visually build business rules as decision trees via drag-and-drop, and immediately deploy the completed tree to chatbots, KakaoTalk auto-replies, or internal Slack bots. It also provides an AI conversion feature that automatically generates decision trees from uploaded Excel condition tables, and a validation engine that automatically detects rule conflicts and omissions.

Target: Owners and operations managers of small businesses in e-commerce/service industries with annual revenue of 100 million to 1 billion KRW ($75,000-$750,000).

Revenue Model: Premium model: Free (3 trees, 100 executions per month), Pro: 39,000 KRW ($29) per month (unlimited trees, 5,000 executions per month, KakaoTalk integration), Business: 99,000 KRW ($74) per month (API integration, team collaboration).

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

2.0/5

U Urgency

4.0/5

M Market

3.0/5

R Realizability

4.0/5

V Validation

3.0/5

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

24.4/25

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 (56/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

7.0/10

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

Frontend [medium] Backend [medium] AI/ML [low]

Dashboard