How We Used AI to Successfully Localize 1 Million Words Within a Month and Cut Costs in Half
If you’re in the tech industry, you know how dynamic and competitive this landscape is.
The ability to develop and launch new products swiftly is not just an advantage — it’s a necessity. Staying ahead requires a combination of continuous learning, innovation, networking, agility, and the strategic adoption of emerging technologies.
This is even more obvious within China’s tech landscape.
Yes, speed is more important than anything in China. That’s how DeepSeek achieved quicker model development and potentially lower development costs compared to ChatGPT. Or how Chinese EV makers have conquered the global market within a few years.
Time is Money, But Also Money is Money!
Operating on a global scale, rapid localization isn’t just a nice-to-have — it’s essential. Because if you don’t launch quickly, your product will likely die the worst possible fate.
Ten years working at several Chinese tech companies, I quickly learned that delivering high-quality localized content on a tight schedule was one of the industry’s biggest challenges.
But what if there were a way to not only speed up the process but also slash costs dramatically?
When I first stepped into the role of managing the localization team at a major Chinese video game developer, my priority was to create a more efficient, cost-effective localization process. This is the story of how we harnessed AI to localize a video game with over 1 million words into four languages in just one month and cut our costs in half.
The Challenge of Localization in the Gaming World
Video games are more than just entertainment; they’re a gateway to immersive, culturally rich experiences. To truly connect with a global audience, games need to speak the language of their players — not just in words, but in art, voice, and cultural nuances.
Traditionally, the localization process has been a lengthy and expensive undertaking. It involves translating complex narratives, adapting art assets, integrating voiceovers that resonate with local audiences, and finally, ensuring the glocalized content is culturally and functionally appropriate.
At the company, every new product brought with it the daunting task of localizing massive volumes of content into multiple languages fast. With deadlines looming and the pressure to reduce expenses while maintaining quality mounting, it was clear that we needed a breakthrough.
A Journey of Discovery: Embracing AI
As a game localization manager, you want to maintain the authenticity of your game’s narrative and aesthetics while dramatically speeding up the localization process.
What’s the best way to deliver that? You’ve guessed it, artificial intelligence (AI) can be a huge help.
But, getting AI to localize a large amount of diverse content well isn’t always easy.
With AI tools hypedly everywhere, you might think a typical ‘set-it-and-forget-it’ tool will do the job, but that usually leads to a riskier and more time-consuming output.
There’s a better way.
A novel approach that systematically leverages AI in a very controlled yet innovative way.
The process was unlike anything I’d attempted before. Instead of relying solely on human translators and artists, I decided to integrate AI into every step of the localization pipeline. It’s like creating a human-machine localization team.
Building the Human-Machine Collaboration
One major mistake people now make when automating their workflows lies on how they assign duties and responsabilities between humans and machines.
Machines are not here to do everything for us; they are here to support and enhance us.
A systematic, well-structured collaborative process tells every side exactly what they need to do and when they’ll do it, ensuring that every element is handled.
Without it, you’re left with an ‘AI-misusing’ team that fails to impress.
Beyond Post-Editing
If your team is only involved in machine-generated content post-editing, then you are doing it the wrong way.
Despite the impressive capabilities of AI systems, humans remains at the heart of the process. Human experts can define human-machine team roles, assign tasks to machines, and refine the machine-generated content, while machines handle massive and routine tasks.
This synergy between human expertise and AI efficiency is what truly makes the process successful.
This is even more crucial when streamlining localization workflows.
Here are the 3 phases that I followed to build an efficient, cost-effective human-machine partnership:
Pre-Production: The first thing you need to do is to break down the process into smaller tasks, defining each side’s duties, and identifying all the necessary activities and deliverables. Especially, preparing source materials before production is crucial — it improves the quality of the output, thereby reducing the need for extensive post-editing. When you know exactly what you want, the output hits the mark every time.
Production: Without the skill of humans, machines are useless. Through well-written prompts, you tell the AI exactly what you need, ensuring that every detail is covered. This clarity prevents the AI from straying and delivering generic responses.
Post-Production: Despite their many strengths, machines face significant challenges. Throughout each step, human oversight ensures that the final product meets the necessary quality standards.
This partnership not only enhances quality but also improves productivity and cost-effectiveness.
I’ll explain exactly how to do this step by step.
Breaking Down the AI Localization Process
Our AI-driven production process was built around a series of carefully crafted, step-by-step prompts. Each prompt was designed to guide the AI through a specific task, ensuring that while the machine handled the heavy lifting, human oversight remained integral at every step.
When integrating AI in your localization workflow for the first time, you need to start from something that can be reviewed easily, such as graphics.
1. Art Generation
Localization isn’t just about words — it’s about visuals too.
Our process involved using AI to adapt in-game art assets to better suit the regional aesthetics of the US, France, Korea, and LATAM markets.
This was easier, because our art team had already integrated AI into their work. 80% of all original graphics of the game had been created using AI.
What my team did, was just gathering the info and referrence resources of different regional aesthetics and submitting them to our art team, then reviewing the AI generated images. (You can also get my regional aesthetics sheets for free from the Glocalingo website.)
By inputting design guidelines into the system, the AI generated localized visuals that respected both the original design and the cultural context of the target market.
The second step was to instruct the AI to transcreate overlay texts from Chinese into English, French, Spanish, and Korean. Detailed prompts helped maintain originality while making it culturally appropriate. They ensured that the generated content was unique, engaging, and SEO optimized.
2. Text Translation
The first step was to integrate DeepL Pro’s API in our translation management system. Then, by crafting precise prompts that accounted for terminology, context, tone, cultural nuances, and text limits, we were able to generate translations that preserved the intended meaning and style.
Then, our linguists post-edited the machine-generated translations to ensure accuracy, fluency, and cultural appropriateness, resulting in a final product that is of high quality.
This comprehensive guide here will take you through a series of actionable strategies I used to transform our translation process.
3. Voiceover Generation
This step was perhaps the most exciting: generating localized voiceovers.
Working with local voiceover studios was too challenging. It was not only expensive but also affecting our deadlines. That’s why we developed an AI dubbing software.
By leveraging the AI’s capabilities in speech synthesis, we created natural-sounding voiceovers in each target language. The AI’s ability to process vast amounts of data in seconds was a incredible, reducing what used to take weeks of manual work into mere days.
This not only streamlined the production process but also allowed us to maintain a consistent auditory experience across different regions.
4. Language Quality Assurance (LQA) Testing
Quality is non-negotiable. But, running LQA tests is an expensive, time-consumming task. That’s why we developed an AI-powered LQA testing tool.
Once the translations were complete, the LQA tool was instructed to run LQA tests, by finding all cosmetic and functional issues, and making test reports. These AI-powered tests also checked for consistency, tone, and cultural appropriateness, flagging potential issues for human review.
After the correction of flagged issues, my team’s LQA testers run regression tests themselves. This final step ensured that while the AI was efficient, it didn’t compromise on quality.
Overcoming the Fear of Change
Embracing such a radical shift in our workflow was not without its challenges. Particularly for the translation part, there was initial resistance — from both within the team and other departments — who were wary of replacing human expertise with machine efficiency. However, the journey taught us several key lessons:
1. Start Small and Iterate
Rather than overhauling the entire process overnight, we started with one of the existing games as a pilot project. Additionally, instead of jumping over to translation directly, we started with graphics and voiceovers — areas that can be fully reviewed quicky. This allowed us to test the AI tools in a controlled environment, make necessary adjustments, and build confidence within the team.
2. Invest in Communication and Training
Clear communication was critical. In the beginning, my boss wanted us to start with translation directly, but it was too risky. We held regular communication and brainstorming meetings to ensure everyone understood the benefits and limitations of the AI technologies. By involving all internal stakeholders in the process, I fostered a culture of innovation and openness to change.
3. Keep the Human Touch
The success of our process hinged on maintaining a strong human presence. The AI technology is a tool — an incredibly powerful one — but it was our well-structured duty assignment, creative judgment, cultural understanding, and quality assurance that elevated the final product. This hybrid approach reassured everyone that technology was here to complement, not replace, human talent.
4. Measure, Learn, and Improve
Every new process comes with a learning curve. I tracked every stage of the workflow, gathered feedback, and continuously refined our prompts and procedures. This iterative process not only improved our outputs but also built a solid framework that could be scaled in future projects.
The Results: Transforming Challenges into Triumphs
The impact of our new process was both immediate and profound. Within just one month, we localized over 1 million words into four languages — a feat that previously would have taken much longer and cost significantly more.
More specifically,
Speed: Traditional methods would have required at least four months to achieve the same output. Our AI-driven process compressed this timeline dramatically.
Cost Savings: By automating key parts of the workflow, we managed to reduce our localization costs by 50%.
Quality: With the AI’s precision and real-time LQA testing, the localized content were consistent across the board and maintained a high standard of quality, ensuring that players received an experience that was both authentic and engaging.
Team Efficiency: The human team could focus on creative and strategic tasks, rather than being bogged down by repetitive translation and testing works.
Employee Satisfaction: Repetitive tasks often lead to boredom and potential fatigue. The automation of our localization process not only improved efficiency and accuracy but also increased our team satisfaction.
The results didn’t go unnoticed. My boss was thrilled with the rapid turnaround and cost savings, and the quality of the localized content received accolades from players.
Why Every Creative Team Should Embrace AI Localization
The success of our AI-driven localization process is not just a win for our company — it’s a wake-up call for the entire tech industry.
For teams looking to disrupt the market, embracing innovative processes like AI localization can be a remarkable. Benefits that speak for themselves:
Cost Efficiency: By automating repetitive tasks, campanies can allocate resources to other critical areas like product development, marketing, and customer engagement.
Speed and Scalability: Rapid localization opens doors to new markets, enabling companies to launch products globally without lengthy delays.
Quality and Consistency: With AI’s ability to maintain high standards across multiple projects, languages, and regions, companies can ensure that their products meet global expectations.
Looking Ahead: The Future of Localization
My journey with AI localization is far from over. As technology continues to evolve, so too will the capabilities of my approach.
The future holds exciting possibilities:
Enhanced AI Capabilities: Continuous advancements in natural language processing and machine learning will only improve the accuracy and nuance of glocalizations and multilingual operations.
Greater Integration: I envision a future where AI seamlessly integrates with all aspects of game development and publishing — from narrative design to real-time player feedback.
Broader Applications: The lessons learned from our localization process can be applied to other industries where rapid, high-quality content adaptation is crucial.
The localization process I implemented is just one example of how AI can revolutionize traditional workflows. From content creation to quality assurance and even customer service, AI has the potential to transform various aspects of business operations. This is exactly what I am building at Glocalingo — an AI-powered ecosystem that handles multilingual, cross-cultural services.
Conclusion: Embrace the Future of Localization
Reflecting on our journey, it’s clear that the integration of AI into our localization process was a turning point. For tech startups and creative teams alike, the message is simple: the blend of human creativity with AI efficiency can lead to breakthroughs that redefine industries.
The future of localization is here — and it’s powered by innovation, collaboration, and a bold willingness to embrace change. As we continue to innovate, we’re excited to see where this journey takes us next.
Have you experimented with AI in your workflows? What challenges and successes have you encountered? Feel free to share your thoughts on how AI is reshaping your industry in the comments.
