Hygraph has recently partnered with Etals, an AI content platform that helps eCommerce brands create, optimize, and scale localized content automatically. Our goal is clear: bring governance-first AI localization to teams shipping content across markets. Hear from Etals CEO Jimmy Bergstedt on the best practices for AI localization.

As organizations expand into new markets, localizing digital content is key to driving engagement and competitive differentiation, but manual localization can't scale cost-effectively.

AI localization tools let developers and marketers rapidly transform large volumes of source content, freeing humans to focus on reviewing AI-generated output. Understanding how AI localization works, its challenges, and best practices is key. That's the thinking behind our partnership: Hygraph and Etals are teaming up to help organizations deliver engaging, localized experiences at scale.

#What is AI localization?

AI localization is the process of using a large language model (LLM) and an automated workflow to adapt digital content for one or more languages, regions, or markets.

With AI localization tools, organizations can rapidly translate text, generate culturally accurate multimedia, ensure the use of correct currencies and payment methods, and optimize user experiences for particular audience preferences.

#What does AI localization do?

AI localization enables organizations to produce more extensive changes, quickly and efficiently. Depending on the scope of each project, AI localization might include:

• Language translation: AI localization tools can convert not only text but also audio content into multiple languages. Teams can generate new video voiceovers, create translated subtitles, and even dub and lip-sync audio within videos when humans are speaking.

• Cultural adaptation: AI can modify content for particular cultures. From currency, date formats, and units of measurement to images, idioms, and regional references.

• SEO and GEO localization: With AI localization tools, teams can localize search engine optimization (SEO) and generative engine optimization (GEO) strategies, adapting keywords, tone, and messaging for local search behavior and cultural norms.

• UI and UX localization: AI tools can optimize the user interface (UI) and user experience (UX) to accommodate distinct preferences and requirements for how people interact with digital content. For example, an AI tool could adjust the layout of pages, including the placement of images or other components, for users who read from right to left.

#The benefits of AI localization

There are numerous benefits to using AI for localizing digital content, from accelerating global expansion to mitigating risks.

1. Expanded global reach

Localization (in general) is critical for maximizing an organization’s global reach. While English is spoken or understood more than any other language in the world, about 80% of people speak and understand only other languages. And yet 49.7% of the world’s websites are in English. By contrast, only 1.2% of websites are in a Chinese dialect, despite the fact that nearly 15% of the world’s population speaks or understands Mandarin Chinese.

Organizations that use AI to localize content can accelerate expansion into new geographic regions and connect with previously untapped groups of people. They can produce cohesive translated content that strongly resonates with new audiences.

2. Speed and scale

AI dramatically increases the speed and scale of localization. LLMs can translate thousands of words and adapt audio from numerous videos in minutes or hour. As a result, organizations can deliver multiple localized sites, in new regions, much faster than before. And when updates and additions are required, they can make changes quickly, without having to delay product announcements or company news.

3. Consistency

AI tools can also help ensure that localization does not stray too far from established messaging, terminology, and style across newly created content. By incorporating glossaries and style guides into the process, teams can achieve consistency that would be difficult to maintain when using multiple human translators.

4. Compliance and cultural risk mitigation

Global organizations must continuously navigate the complexities of country-specific regulations and even evolving cultural sensitivities. Complying with the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), for example, requires different notices about data privacy.

Meanwhile, organizations must avoid cultural mistakes like using contested territory names or violating regional cultural social norms. AI systems can help put guardrails in place to prevent mistakes that could lead to fines, blocked sites, or PR disasters.

#What are the challenges of AI localization today?

As content production speed becomes table stakes, governance becomes the real differentiator. The teams succeeding with AI content localization in 2026 treat governance as infrastructure, not a final quality check.

While the capability in translation and localization is real, what's actually moving the needle isn't teams prompting their way through ChatGPT or Claude. Chat interfaces work for one-off tasks, not the structured, repeatable output that scaled operations demand. Many teams have caught onto this and shifted toward agentic content workflows instead.

Since agentic AI entered website localization in 2023, the same pattern has repeated itself. Teams stand up their agents, run the pipeline, and watch impressive volumes of content appear in new languages. Then someone actually reads it, and the cracks show: brand voice drifted, structure got inconsistent. This is AI localization done poorly, and because the output looked finished, it travelled further down the pipeline than it should have.

Poorly governed AI content doesn't just underperform, it compounds. Wrong brand voice across ten markets. SEO signals quietly eroding. Product descriptions that are technically localized but feel like they were written by no one. This is the new technical debt.

This is the real challenge: governing content localization at scale.

Treating AI as a self-serve tool, where individual team members prompt independently, doesn't create scale. Creating agentic flows with agents that don't have the right context creates inconsistency that requires expensive review cycles to fix. Before scaling, every team needs to ask:

• Do we have a single source of truth for brand voice across markets for AI to access?
• Is our GEO/SEO structure documented well enough to be enforced at a system level?
• Can our team collaborate around AI output or is everyone prompting in their own corner?
• What does a failed output actually cost us in time, rework, and missed ranking opportunities?

The teams succeeding with AI localization treat governance as infrastructure, not a final quality check. Brand rules, SEO/GEO frameworks, and approval workflows built in from the start, not layered on when the damage is done.

#Best practices for AI-powered localization

AI can significantly streamline the work of localizing digital content, but AI tools are not quite as simple as clicking a button. A few best practices can help ensure your team is making the most of AI localization tools while reducing potential problems.

1. Design source content for localization from the start

When you know in advance that you will be translating content into other languages and building localized sites, you can avoid issues that might trip up an AI system. First, prepare your CMS to publish content in multiple locales. Next, when creating content, avoid idioms, culture-specific humor, or wordplay that isn’t likely to translate well. Finally, be ready for text expansion, when a target language (such as German) requires more characters to say the same thing as the source language.

Avoid hardcoding the width or height of elements containing text, since those elements might need to grow, or shrink, depending on the target language.

2. Prepare to scale

Chat interfaces like ChatGPT can’t handle localization at scale. Select solutions that build agentic content flows and deliver structured output so you can easily expand your localization efforts.

Implementing a structured content model with a headless CMS can help you make the most of that structured output and maximize scalability. By using structured content, you can localize at the field level. So, if you need to make a change to a single paragraph, button, or product feature, you can retranslate a small section instead of the entire page. You save time and API costs.

3. Establish clear governance, early

Governance is a real differentiator in AI localization. Teams that treat brand rules, SEO/GEO frameworks, and approval workflows as infrastructure from the start will have greater success using AI localization tools than those who bolt them on later.

Like managing multi-brand websites, you need a centralized repository of terms and rules to avoid drift as you produce localized sites. As a first step, provide the AI tool with a glossary of key terms, documented SEO/GEO rules, and a company style guide. Having these single sources of truth will help avoid incorrect translations, missed SEO/GEO opportunities, and brand compliance issues.

Then establish review processes that keep humans in the loop. Implementing human reviews is critical for ensuring that AI-generated results are accurate, consistent, culturally sensitive, and compliant with both regulations and brand rules. Consider using AI to handle the first pass at translations and content adaptation. But then have humans review and modify results. You could focus human reviews on high-impact messaging or sections that the AI system has flagged with a “low-confidence” score.

4. Pilot before scaling

Start with one or two target languages or markets. Assess the quality and the amount of human effort required for reviews. Then refine your workflow—possibly trying other LLMs, modifying prompts, or adjusting the timing of human reviews—before rolling out AI-powered localization to numerous sites.

5. Set up a feedback loop

Invariably, your team will find areas for improvement. You might discover that the AI system mistakenly translates product names, fails to adjust cultural references, or misses style rules. Make sure you capture human-aided corrections and feed them back into the system to reduce issues in the future.

#Hygraph and Etals' new collaboration aims to end bad AI localization

By integrating Etals natively into Hygraph's CMS, brands can connect AI-powered content agents directly into the workflows they already rely on. No migration, no parallel systems. Brand-aligned, SEO-ready, localized content flows into the right place from day one, with governance built in rather than bolted on.

For brands already inside Hygraph, the value is immediate. For anyone asking how to scale content without losing control, it's a model worth paying attention to.