The AI Ready Localizer #8: How to Use AI to Prevent i18n Bugs

A Practical Guide to Context Engineering

In this edition of The AI-Ready Localizer, I want to get hands-on with how AI can help prevent internationalization (i18n) issues before they hit production.

If you caught my last post, I shared real examples of the most common pitfalls — from hardcoded strings to concatenation disasters.

But here’s my question:
Beyond manual source reviews and looping back with dev teams, can we build an AI-powered scanner that automatically flags potential i18n issues in string files and suggests fixes?

Let’s explore that.

Sneak peek of the i18n scanner I’ve been building — catching global bugs before they ship.

An AI-Powered i18n Scanner

I’m building a tool (I started building it in Lovable and I’m now experimenting with it in Replit as well) that:

• Stays in sync with source string files

• Continuously analyzes them for potential i18n risks

• Generates developer-friendly reports explaining the issues, causes, and fixes

• Offers recommendations tailored to each stack and file format

It detects:

• Hardcoded plurals and gendered logic

• String concatenation patterns

• Positional vs. named formatting (e.g., %s vs {name})

• Directionality assumptions (e.g., LTR-only layouts)

• Variables that break grammatical agreement

• Embedded business logic inside translatable strings

It outputs:

• Annotations of risky strings

• Explanation of the issue

• Recommended refactor patterns

• Suggested i18n libraries/tools per stack

But none of this works without the right context.