How AI Is Changing Magento Development — Claude Code + Rector in Practice
When I tell shop owners I can complete a Magento migration in 4 weeks instead of 12, they’re sceptical. That’s a reasonable reaction. The Magento ecosystem has a long history of projects that took longer and cost more than anyone expected.
But the tools available now are genuinely different from what existed three years ago. Here’s what the workflow actually looks like.
Why Magento migrations are expensive
The cost isn’t arbitrary. A Magento 1 to Magento 2 migration involves analysing potentially hundreds of custom files, understanding what each piece of business logic does, finding the M2 equivalent (or building it), rewriting, testing, and doing it all without breaking a live shop.
Traditional agencies charge €15,000-50,000 for complex migrations because it genuinely takes that long with manual methods. A senior developer can spend weeks just mapping what a codebase does before writing a single line of new code.
That’s the problem that’s changed.
Rector
Rector is an automated PHP code transformation tool. The shortest description: find-and-replace for code structure.
It works by applying defined rules to a codebase: rules like "transform this deprecated pattern to the current equivalent" or "upgrade all code from PHP 7.4 syntax to PHP 8.2." For Magento specifically, there are rulesets that handle a significant portion of the M1-to-M2 pattern migration automatically.
The more standardised the original code, the more Rector can handle. For shops built on standard Magento modules with relatively conventional customisations, Rector can transform a large chunk of the migration work without a human touching each file individually.
It doesn’t replace judgement; it eliminates the tedious parts so judgement can be applied where it’s actually needed.
Claude Code
Claude Code is an AI coding assistant that works inside a codebase, not just in a chat window. It can read the entire project, understand module dependencies, trace how customisations interact with core code, and generate M2-compatible replacements from M1 source files.
The practical difference from a generic AI assistant: it has context. It knows what this module does, what it depends on, and what the M2 equivalent architecture looks like. That means it can make recommendations that account for the specific project rather than giving generic guidance.
I wrote about using Claude Code to build a macOS notification tool, a completely different context, but it illustrates how it works as a development partner rather than just an autocomplete tool.
What the workflow looks like
Here’s a simplified version of how an M1-to-M2 migration actually runs with these tools.
Day 1. Claude Code analyses all the custom modules in the M1 installation. It maps dependencies, identifies which modules can be replaced by M2 core functionality or a third-party extension, and flags which ones need custom migration work. This analysis used to take days. It takes hours.
Days 2-3. Rector transforms the PHP code to M2 standards. Claude Code handles the architectural changes that need more judgement: controllers, the observer-to-plugin migration, layout XML to UI components. This runs in parallel, not sequentially.
Days 4-5. Manual review, testing, edge cases. This is where 30 years of Magento experience is actually doing the work. The AI produces first drafts and handles the bulk transformations; a human decides whether they’re right, catches the edge cases, and handles anything that requires understanding the business logic rather than just the code structure.
Comparable scope with traditional methods: 3-4 weeks of developer time, minimum.
What this means for the client
Less time means a lower invoice. That’s the direct effect. But there are a few less obvious ones.
The AI-assisted analysis phase tends to be more thorough than a manual audit because it doesn’t get fatigued. It will read every file, trace every dependency, and flag everything unusual, whereas a developer under time pressure sometimes makes assumptions. I’ve caught things during the automated analysis phase that would likely have been missed in a traditional project and would have surfaced as post-launch bugs.
The faster timeline also means less disruption. A 4-week migration is something a business can plan around. A 12-week migration is a different kind of organisational commitment.
The honest limitations
AI tools accelerate the parts of a migration that are systematic and pattern-based. They don’t handle everything.
Complex business logic, custom pricing rules, unusual checkout flows, integration-specific behaviour, still requires human understanding. If a module does something that isn’t a standard Magento pattern, the AI can describe what it does, but deciding how to translate it to M2 is still a judgement call.
Testing and QA are still critical. AI-generated code needs to be validated. The review step in the workflow above isn’t optional; it’s where quality gets ensured.
I use these tools to be faster, not to skip steps. The deliverable is the same; the path to it is more efficient.
A note on where this fits
This workflow is part of what makes it possible to offer Hyvä migrations at a price point that has a sensible ROI for shops that aren’t at the very high end of the market. When the analysis and transformation work takes half the time, the cost to the client comes down without the quality following it.
For a deeper technical walkthrough of the Claude Code + Rector workflow, I’ll be writing that up on thecoder.io; it’s the right venue for the implementation details.
If you want a realistic picture of what a migration would involve for your specific setup, scope, which parts benefit from automated tooling, rough timeline, I’m happy to take a look.