# Automated I18n Quality for Enterprise Platforms

##### Globalization Readiness

- Linguistic Quality
- Extensibility
- Maintainability
- Time to market
- Portability (standard based)

##### Reactive vs Proactive

re: fix bugs, correct translations, troubleshoot, but costumer will find issues before you

<span style="white-space: pre-wrap;">Prevent bugs, establish best practices that are global ready </span>

##### Using AI out of the box

goose: Agentic vibe coding, but it dose not use ICU, does not deal with data ready for i18n.

LLM-&gt;most common, but statistically wrong.

- Not using standard region codes.
- Assumes only one language per region
- Assumes only two forms for plural
- Sloppy plural(s) construct in some languages
- No gender handling
- Embeds formatting and layout with content
- Content for all locales in a single file
- (not shown)
- Poor phone structure as raw text
- No attempt to find or use libraries for phone, address, or to CU or CLDR

##### Detect Issues in source content

- Before antering the translation pipeline
- Within Atlas, a plafform for managing localization workflows
- <span style="white-space: pre-wrap;">Rulebased linting </span>
- Using 3rd party lib: ilib-lint

Github -&gt; CI（自动实行构建） -&gt; AWS -&gt; Management platform -&gt;Github/CI/Translator vendor

##### Detect issues in source code

- Independent of translatable content
- Much larger dataset
- Build a custom scanner
- Static Analysis + AI
- Many programming language
- Custom integrations

##### i18n using AI + Self-Healing

<span style="white-space: pre-wrap;">Sourcecode I18n self healing using AI </span>[study](https://www.linkedin.com/pulse/source-code-internationalization-self-healing-using-anna-n-schlegel/)

- Scan-train-refine
- Knowend and discovered

##### Going forward with AI

- i18n anti patter development
- Scanning tool development
- Fine tuning results
- AI Training
- Self-healing training
- CI/CD Intergration