Legacy Code AI Migration Auditor

4.20

Derivation Chain

Step 1 AI coding tool proliferation

→

Step 2 Legacy code AI migration consulting

→

Step 3 AI migration readiness automated audit SaaS

Problem

System integrators and IT agencies with 10-50 employees attempting to adopt AI coding tools (Copilot, Cursor, etc.) on existing legacy codebases spend an average of 2-4 weeks per team to assess which modules are suitable for AI auto-completion and which require manual refactoring. Without this assessment, blindly adopting AI tools causes AI-generated code to conflict with legacy patterns, actually increasing technical debt.

Solution

Connect a GitHub/GitLab repository and the tool automatically analyzes code complexity, test coverage, dependency graphs, and coding convention consistency to calculate a per-module 'AI Migration Readiness Score.' Delivers a prioritized refactoring roadmap and AI tool configuration recommendations as a Report.

Target: Tech leads and development team managers at system integrators and IT agencies with 10-50 employees

Revenue Model: Per Transaction pricing: single repository analysis at $150. Monthly Subscription for up to 3 repositories at $225/month. Additional repositories at $75/month each.

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

4.0/5

M Market

4.0/5

R Realizability

4.0/5

V Validation

5.0/5

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 (68%)

Tech Complexity

29.3/40

Data Availability

18.8/25

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 (72/100)

Competition

10.0/20

Market Demand

20.0/20

Timing

16.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

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] AI/ML [medium] Frontend [low]

Dashboard