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This article focuses on Helix, an internal tool leveraging LLMs to automate and refine the migration of a large-scale mobile application from React Native to native code through iterative checkpoints. It highlights how structured guardrails and human-in-the-loop feedback loops are essential for achieving high-quality, maintainable results in complex software engineering tasks driven by AI.
Helix: The internal tool powering our Shopify app's native migration

We’re moving our mobile apps from React Native back to native Swift and Kotlin. We’ve already done it with Shop, rebuilding and publishing the app in just 12 weeks. Now we’re applying what we’ve learned to the Shopify App, our largest app with more than 300 screens. We’re using LLMs to rebuild it because they are really capable now; but getting consistent, high-quality, and maintainable results out of the box is difficult. They need tooling and guardrails. That’s why we built Helix. Helix is a set of tools and skills that help LLMs migrate features and screens from the React Native app while following a highly opinionated architecture we designed for the new native apps. Most tools try to gather as much information as possible, turn it into specs and task files, implement the whole thing, and hope the first result works. The engineer gets a huge chunk of code with everything left to test. Helix takes a different approach. It doesn't expect the first output to be correct.…
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