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ModernizeAI reads your legacy codebase, builds a transparent readiness assessment, and drives AI-assisted transformation toward a modern stack — with a human reviewing and approving every change along the way.

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Capabilities

One workbench, three stages of a modernization project

Each capability maps to a phase your migration already goes through — not a generic code-generation demo.

Deterministic codebase analysis

Languages, frameworks, dependencies, and security findings are mapped from real static analysis — every score comes with the evidence behind it, not a guess.

Assessment, architecture & roadmap

Claude proposes a modernization strategy, a target architecture (with diagrams), and a phased plan — grounded in the discovery evidence, not generic advice.

Human-reviewed code transformation

Select a file, describe the change. The AI proposes a diff with an explanation and impact analysis — a person reviews and approves before anything is applied.

How it works

Every AI change is reviewed by a person before it lands

Every AI-generated change is carefully reviewed, refined, and approved by a human to ensure quality, accuracy, consistency, and reliability.

1

Upload the legacy codebase

A repository archive, extracted and scanned inside a sandboxed, path-guarded workspace.

2

Review the discovery report

See the technology inventory, findings, and transparent readiness scores before any AI call is made.

3

Ask for a transformation

The AI proposes a change as a reviewable Before / After / Diff, with the reasoning laid out alongside it.

4

A human approves — or rejects

Nothing is written to the codebase without an explicit accept. Every decision is recorded in the audit trail.

Human-in-the-loop

Nothing ships until a person says so

The AI's job is to propose — clearly, with its reasoning shown — and a reviewer's job is to decide. ModernizeAI is built around that boundary, not around removing it.

Human-in-the-loop by design

Every AI-proposed change requires an explicit human approval before it is applied to the codebase.

Reviewable, revertible diffs

Changes are shown as a line-level diff with a plain-English explanation and impact analysis.

Full audit trail

Every proposal, approval, rejection, and revert is timestamped and attributable.

Compliance by construction

GIGW 3.0, WCAG 2.2, and OWASP guidance are baked into every recommendation the AI makes.