How AI is changing design-stage localization

What is design-stage localization?

Design-stage localization treats localization as a critical step in your design and development cycle instead of a post-launch task. 

Your localization, design, and development teams work in tandem. Designers create mockups for multiple languages, linguists share translated files to validate these layouts, and your team identifies issues before writing a single line of code. Once validated, developers build features with translations already in place. 

Here’s what the design-stage localization workflow looks like in action:

This approach speeds up your localization process and eliminates costly rework cycles after you’ve launched a project.

How AI can impact your design-stage localization setup

AI-powered design-stage localization is rapidly becoming the gold standard for many software teams. Translation can happen as fast as design iterations and development, creating truly parallel workflows.

Let’s break down five meaningful ways AI can impact your design-stage localization process.

1. Instantly test your designs in multiple languages before development

German text is often longer than English. Arabic navigation reads right-to-left. And Chinese characters expand vertically. Look at Canva’s homepage across four languages, for example. The layouts and spacing change for each language:

Design-stage localization makes it easy to adapt your layout to nuances like these for different target languages before a single line of code is written. It saves you from expensive rework cycles and project delays.

But here's the big challenge: getting translations traditionally takes days or weeks. When you're iterating quickly on mockups, this waiting period makes it difficult to validate your design layouts for different languages.

AI translation removes this bottleneck.

For starters, AI can translate strings into multiple languages almost instantly. So, what used to take weeks of back-and-forth with translators and reviewers can now happen in minutes.

More importantly, when your localization software is connected to your design tool, AI translations can fast-track the process even more. That means, a designer working on a new landing page can:

• Share specific text strings for AI-generated translations
• Directly pull these translated files into their mockups
• See exactly how each language affects its layouts

How Lokalise’s design plugins work with AI translation

Lokalise integrates directly with Figma, Adobe, and Sketch, cutting out the back-and-forth between designers, developers, and translators. Designers can quickly share design mockups, get translated files, and iterate on these mockups for each language.
Let’s understand how this works with the example of Lokalise’s plugin for Figma:

• Designers push strings from Figma to Lokalise: When your designs are ready, push all text strings to Lokalise. Add screenshots so translators (or Lokalise’s AI system) see exactly where each string appears in the interface.

• AI translation on Lokalise: Lokalise’s AI system translates strings into your chosen languages based on the visual context and existing guidelines like translation memory, style guide, and glossary. Up to 95% of translations are publish-ready. Your linguists review high-value strings and adjust where needed.

• Pull translations back to Figma: Once AI translations are ready in Lokalise, pull these files into Figma. Switch between different languages to test and adapt your layouts for each one.

Finally, send both translation keys and validated design layouts to development. This way, your designs work across languages from day one, and you avoid post-release bugs in the localized user experience.
 

2. Use design context already built into your workflow

When you push text from your design tool to your localization platform, AI taps into existing context to deliver translations that match your tone and quality benchmarks.

For example, AI cross-references multiple data sources available in your localization setup:

• Translation memory: To deliver consistent translations. If you've translated “checkout” as “Kasse” in German before, AI uses the same term.
• Screenshots: To see where every key appears in the interface. AI sees that “Submit” lives on a narrow button, not a spacious form header.
• Character limits: To prevent design overflow. A 60-character English string won't become a 95-character German translation that breaks your layout.
• Glossaries: To follow your terminology guidelines. If your brand uses “sign in” instead of “log in,” AI follows that rule.

This makes AI translations particularly valuable for design-stage localization where speed matters most.

Think about it: During the design phase, you need quick translations to validate layouts and test user flows. AI handles this quickly, while your translators focus on polishing high-value content once designs are locked in. 

As a result, your design process moves at full speed without creating a bottleneck.

Besides, modern localization software like Lokalise delivers context-aware AI translations. That means, AI translates text with complete awareness of how, where, and why text appears in your interface.

Here’s what this context-aware translation looks like in action.

Let’s say you have a dialog box with three strings:

• Header: Delete file?
• Body text: This action cannot be undone
• Button labels: Cancel / Delete

Context-aware AI translation considers this dialog box as one cohesive component instead of isolated strings. The system understands design constraints like spacing, font sizes, and container dimensions, then factors these into translation choices. 

For example, if a button has an 80px width, AI won't suggest a 15-character German word that overflows the container. It sees the button as it appears in your interface, understands the character limit, and references your translation memory to translate this string.

3. Keep localization and product development in sync

The traditional localization process creates natural bottlenecks. 

Developers ship new features weekly. Designers iterate on mockups frequently. But translators work in batches where you send out a batch of strings, wait for turnaround, then integrate translations in a separate release cycle. 

AI translation within your design-stage localization process can break this pattern. 

It allows your localization workflow to run parallel to the development cycle. When developers push code or designers adjust mockups, translations update immediately. So, linguists can review and refine changes in context, not weeks later.

Here's what this looks like in practice. 

You change a button label from “Submit” to “Confirm” in Figma. AI regenerates translations across all target languages within seconds. The system flags the change for linguist review if it's customer-facing or high-value, but the translated strings are already available for the next build.

How the right platform supports continuous localization

Continuous localization keeps translations in sync with product development by automating the flow between design, development, and translation teams. 

Modern localization platforms like Lokalise connect directly to your design and developer tools to create a continuous translation workflow:

• When a designer updates text in Figma, that change syncs to the localization platform automatically
• When a developer commits new strings to GitHub, AI translates them before the next build runs
• When a linguist approves a translation, it deploys to production without manual handoffs

This workflow eliminates the context-switching delays that traditionally slow down releases. 

Put simply: You're not blocking the dev team while waiting for translators to finish a batch of translations. You're not rushing linguists to hit an arbitrary deadline that affects their output quality. 

With the continuous localization approach, translation happens in parallel with design and development at the same velocity as your team ships features.

4. Let your linguists focus on what actually matters

In addition to speeding up your translation process, AI can also handle quality assurance. As a result, your linguists spend time where their expertise actually matters.

AI-powered translation scoring systems evaluate translated output against specific quality criteria and assign each one a score. So, instead of linguists manually reviewing every translated string across every language, AI scoring flags specific files that need their attention.

Lokalise's translation scoring system uses the MQM (Multidimensional Quality Metrics) framework to score every translation on a 0-100 scale based on grammar, spelling, fluency, terminology accuracy, and meaning preservation.

The scoring algorithm evaluates multiple factors:

• Does the translation match similar strings in your translation memory?
• Does it use approved glossary terms?
• Does the length fit the design constraints?
• Is the sentence structure natural in the target language? 

Translations scoring above 80 are good to publish without human review. Translations scoring below 20 get flagged for linguist attention, specifically for better cultural adaptation and brand alignment.

How translation scoring helps design-stage localization

Translation scoring is an automated process of reviewing the quality of AI-translated text based on factors like accuracy, consistency, fluency, and more. For design-stage localization, this scoring mechanism creates a quality layer to validate designs quickly.

Use these scores to speed up translations with AI and:

• Focus human effort where it matters: Your linguists spend time on complex, high-impact translations. AI handles routine navigation labels and form fields that follow established patterns.
• Cut review time and cost: With translation scoring, teams can handle low-priority languages that were previously cost-prohibitive and ship faster without sacrificing quality.
• Get instant, data-driven QA: Linguists see the scores in real-time and find specific issues detected from every translated string, like the ones highlighted in this screenshot.

The bottom line: When you’re launching in 15 different languages, you can’t manually translate and review every string in every language (unless you have a massive team). That’s where translation scoring helps you prioritize only those issues that need more attention. 

5. Balance quality, speed, and cost of localization

Not all content needs the same level of translation sophistication. A “Save” button doesn't require the same care as tooltips in your onboarding flow or error messages that explain why a payment failed. 

AI handles this distinction automatically to keep costs low and quality high.

How? Through intelligent LLM routing. 

Instead of using the same translation engine for every request, AI intelligently routes different content types to specialized language models based on complexity, context, and quality requirements. Put simply, your localization platform analyzes each string and decides which AI model should handle it.

How intelligent LLM routing works in localization

Intelligent LLM routing for localization analyzes each string and automatically assigns it to the most appropriate language model based on complexity and context. Instead of using a single AI model for everything, the system routes different content types to different models optimized for specific translation tasks.

The routing logic considers factors like string length, presence of variables or placeholders, translation memory match percentage, and content type to make decisions automatically:

• Simple strings: Repetitive strings like “Save,” “Cancel,” and “Next” go to fast models optimized for consistent, predictable output. These strings have high translation memory matches and limited cultural variation, so they don't need sophisticated language processing.
• Complex strings: Marketing copy, error messages, onboarding tips, and value propositions are routed to sophisticated models trained on conversational language and context-aware translation. These strings benefit from models that understand tone, cultural context, and natural language flow.

Intelligent LLM routing for localization means every piece of content gets the right translation approach matched to its purpose. The system automatically balances quality, speed, and cost without localization managers manually sorting thousands of strings or choosing models for each translation task.

Level up your design-stage localization workflow with Lokalise

AI can remove the waiting time that previously kept localization separate from design and development. 

Context flows directly from design files to translators without manual documentation. And translation happens instantly, with built-in quality checks, for designers to validate layouts. 

You can build a continuous and streamlined workflow instead of relying on manual handoffs.

Lokalise integrates AI-powered localization directly into your design and development tools. 

The platform uses intelligent LLM routing to select the best translation engine for each string. Context-aware AI also pulls visual information from your designs and taps into existing guidelines. And automated quality scoring will flag the translations needing human review.

Sign up for a 14-day free trial to see Lokalise’s AI system in action.