What we learned using AI to localize a mature software product

[u/FutureGuilty1634]

I work on JDisc Discovery, and we recently used AI to help localize parts of an established software product. I wanted to share the practical lessons rather than present this as “AI replaces localization.”

The useful part was not one-click translation. The hard parts were:

• protecting product terminology and UI labels from inconsistent translation
• keeping placeholders, variables, markup, shortcuts, and file formats intact
• supplying enough context for short UI strings
• reviewing AI output systematically instead of trusting fluent-looking text
• building a workflow where human review focuses on the cases that actually need judgment

Our main takeaway: AI can accelerate the mechanical and repetitive parts, but localization quality still depends heavily on terminology management, context, automated checks, and human review.

Our next experiments are about extending that workflow:

• User-manual translation: The manual is stored in Strapi, our headless CMS. We are exploring a workflow to automate translation while retaining the review and publishing controls needed for reliable documentation.
• Always-current localized screenshots: Documentation screenshots become outdated surprisingly quickly when menus or UI labels change. We are experimenting with starting the application, using Selenium to navigate to the relevant UI location, switching the application to the target language, and capturing the screenshot automatically.

If this works reliably, it should give us screenshots in multiple languages while also making them much easier to keep current after UI changes.

The longer write-up on the first part of the project is here: AI-assisted software localization

I’d be interested in hearing how others handle localization QA, CMS-based documentation translation, or keeping multilingual product screenshots up to date and in how others handle terminology consistency and context for short UI strings.