Smart AI Modernization — C# WinForms to React, Migrated Deterministically

Why Experience Beats Raw Model Power

Pure LLMs Can Write Clean Code.
They Can't Migrate Your Business.

Claude and ChatGPT can absolutely generate a clean-looking React component. What they can't do is know that a keypress handler on your shop-floor screen is wired to a barcode scanner over a serial port, or that the "Please Wait" dialog your operators rely on is load-bearing for a hardware timeout. That's not a model capability problem — it's an experience problem.

Pure LLM Migration

Confident. Fluent. Wrong in Ways You Won't Catch Until Production.

✗ Hallucinates hardware and device integration code — scanners, printers, PLCs, serial/USB devices
✗ Invents plausible-looking SSO and auth flows that don't match your actual identity provider
✗ Silently drops or rewrites custom UI messaging, dialogs, and validation copy
✗ Guesses at third-party library behavior instead of mapping it correctly
✗ No governance, no repeatability — same prompt, different output next time

Smart AI Modernization

Built From Thousands of Real Migrations, Not a Demo Prompt.

Governance rules encoded directly into the deterministic engine for known device, SSO, and third-party patterns
Highly specialized agents and prompts built for the specific edge cases each engagement surfaces
Deterministic output for the 80–90% of the codebase that doesn't need judgment
AI reserved for the cases that genuinely require semantic understanding
Enterprise-grade code that ships to production

💡

We don't generate "AI slop." With experience from thousands of real-life migration projects, we know exactly where pure LLMs fail — and we've built the governance and specialized tooling to handle those edge cases correctly, every time.

Built for Enterprise Scale

Above 50,000 Lines of Code,
Pure LLM Accuracy Collapses — and Token Costs Skyrocket

RepoMod-Bench (Li et al., February 2026) — a benchmark study across 21 real-world repositories and 1.6M lines of code — revealed a stark performance cliff that directly determines whether your migration succeeds or fails.

LLM Success Rate by Codebase Size

Under 10,000 lines 90%+

LLMs excel: entire codebase fits in context window

10,000 – 50,000 lines ~45%

Mixed results: partial context, inconsistent output

Over 50,000 lines 15.3%

Frequent hallucination, broken cross-file dependencies

Source: Li et al., RepoMod-Bench (arXiv:2602.22518, Feb 2026) — 21 real-world repositories, 1.6M LOC, 11,616 tests

The Real Cost Isn't the Subscription

It's the Re-Reads.

Pure LLM migration tools re-submit large chunks of your codebase into the context window on every iteration. Every time the model loses track of a cross-file dependency, it re-reads files to recover context it should already have. On a 50,000-line application that's thousands of redundant tokens per fix. On a 500,000-line application, it stops being economically viable.

Our engine is mostly deterministic. The codebase is indexed once. LLM calls are scoped narrowly — generate this semantic model, validate this test, review this diff — never asked to hold your entire application in working memory.

Token spend stays flat as codebase size grows — not exponential
No context-window ceiling: deterministic indexing bypasses it entirely
The same guaranteed methodology at 50,000 lines or 2 million lines
Predictable, quotable pricing — not an open-ended LLM API bill

How It Works

A 6-Stage Methodology
Built to Ship, Not Demo

Every stage has a clear owner — deterministic engine, LLM, or your team — so nothing about the migration is a black box.

1

Indexing

Deterministic

Deterministic parsing of your entire codebase — every form, control, event handler, module, and reference — producing a complete index before a single AI call is made. Nothing is inferred; everything is parsed.

2

Analyze

LLM

The LLM analyzes the index and generates a Semantic Model — a structured JSON representation of what each screen and workflow actually does in business terms, not just what the code says.

3

Review

Human-in-the-Loop

Your subject matter experts review and approve the Semantic Model before any code is generated. This is the checkpoint that keeps device integrations, SSO quirks, and custom messaging from ever being guessed at.

4

Transpile

Deterministic

Deterministic code generation using your original source and the approved Semantic Model produces three things: Domain APIs, a Backend-for-Frontend (BFF) proxy, and a 1:1 React UI. Rule-based, repeatable, auditable.

5

Test Case Generation

LLM

LLMs generate unit and functional tests from the approved Semantic Model to validate that the new application behaves like the original — covering the workflows and edge cases your SMEs already signed off on.

6

Code Review

Reserved for Key Validations

An LLM generates a report comparing the original application to the new one. This is the most token-intensive step in the pipeline, so we scope it deliberately — running it on the workflows and modules that matter most, not the entire codebase indiscriminately.

🎨

UX/UI redesign and production deployment are secondary steps. Once the 6 stages deliver a working, validated React application, visual redesign and go-live deployment are handled deliberately after — so the core tool stays focused on what it does best: accurate, deterministic migration.

Who We Are

The Pioneers of Legacy Modernization,
Now with AI

Our team has spent over 20 years building the tools and methodologies that defined enterprise legacy modernization. We didn't pivot to AI — we integrated it where it genuinely improves on what deterministic tools do best.

A

Artinsoft

Legacy Migration Platform

Founded by our team, Artinsoft became the industry reference for automated legacy code migration — migrating Visual Basic 6, PowerBuilder, and early .NET applications for Fortune 500 clients worldwide. The core tool engineering expertise that made Artinsoft successful is the foundation of our approach today.

M

Mobilize.NET

Modern Migration Platform

The evolution of that work — Mobilize.NET brought automated migration to the modern .NET era, handling WinForms, ASP.NET Web Forms, VB.NET, and complex multi-tier .NET applications at enterprise scale. The experience of hundreds of real migration engagements lives in our tooling.

🎯

20+ years of migration experience means we've seen every failure mode. We know exactly where pure automation breaks down, where manual work is unavoidable, and where AI creates genuine leverage — because we've done this at scale, before AI, and we know what "done" actually looks like for enterprise applications.

Additional Services

Also Running ASP.NET Web Forms?

The same deterministic-first philosophy — now applied to WinForms and React — started with Web Forms. If your legacy estate includes .aspx applications, we bring the same 20+ years of migration tooling to that platform too.

Deterministic Analysis

Full static analysis of your solution: every dependency, every postback chain, every data binding, every business rule — parsed deterministically before a single line of code is transformed.

Rule-Based Transformation

80–90% of your codebase is migrated through proven, battle-tested transformation rules. Predictable output. Verifiable results. No hallucination, no guesswork.

AI Semantic Validation

LLMs validate that the migrated code preserves the semantic behavior of the original — not just syntactic equivalence, but functional equivalence at the business logic level.

Explore Web Forms Modernization →

C# WinForms to React —
Modernized Deterministically, Not Guessed by an LLM

Pure LLMs Can Write Clean Code.
They Can't Migrate Your Business.

Confident. Fluent. Wrong in Ways You Won't Catch Until Production.

Built From Thousands of Real Migrations, Not a Demo Prompt.