B

Public Procurement Bidding Coach for Small Business Owners

3.50

Derivation Chain

Step 1 Digital transformation of public administration
→
Step 2 Expansion of electronic bidding in public procurement
→
Step 3 Support for small business owners to auto-generate bidding documents

Problem

Small business owners with annual revenue of 100 million to 1 billion KRW (approx. $75,000 to $750,000) who want to participate in public procurement bidding on the Korea Online E-Procurement System (나라장터) must prepare an average of 12 types of documents, including proposals, price calculations, and performance certificates. Interpreting the bid notice alone takes 3-5 hours, and document preparation takes 20-30 hours. As a result, the bid abandonment rate exceeds 60%, undermining the effectiveness of SME preferential treatment policies.

Solution

Automatically parses bid notices from the Korea Online E-Procurement System to generate a required document checklist, auto-drafts proposal outlines based on analysis of past winning proposals, and automatically matches price calculation templates. Differentiation: industry-specific templates (IT/construction/services) and pre-bid probability diagnosis.

Target: Small business owners in IT services/software with annual revenue of 100 million to 1 billion KRW, and representatives of micro SI (system integration) companies with 1-5 employees.
Revenue Model: SaaS monthly subscription: 49,000 KRW/month per account (approx. $36.75), additional proposal generation per bid: 30,000 KRW (approx. $22.50).
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
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 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 (73%)

Tech Complexity
29.3/40
Data Availability
23.3/25
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 (54/100)

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
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

Data Pipeline [medium] AI/ML [medium] Frontend [low]
Dashboard