AI Code Handoff Tool

3.15

Derivation Chain

Step 1 AI coding tool proliferation reduces code-writing share

→

Step 2 Context loss in AI-generated code

→

Step 3 AI code decision-record automation tool

Problem

Code rapidly generated by AI coding tools lacks the decision-making context behind 'why it was implemented this way.' During team transitions or handoffs, understanding the design intent of AI-generated code takes 2-3x longer than before, and misinterpretation causes bug recurrence.

Solution

Automatically captures AI coding session prompts, responses, and edit history to generate 'decision cards' per code block. Integrates with Git commits to build a searchable knowledge base of code change rationale. During handoffs, the full AI decision history for any file is viewable at a glance.

Target: Startup dev teams of 5-30 people, IT agencies and SI firms that have adopted AI coding tools

Revenue Model: SaaS Monthly Subscription at KRW 7,900 (~$5.90) per developer/month. Free for 3 or fewer users. 20% discount on Annual Subscription

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

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.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (69%)

Tech Complexity

29.3/40

Data Availability

19.4/25

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 (52/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

7.5/15

Pick-Axe Fit

9.0/15

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

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

Dashboard