Evolving your localization strategy
Hey there!
What a month July has been. I hope you'll indulge my British-ness for a moment and allow me to open this month's dispatch with a comment on the weather; it has run the gamut over the last few weeks, from heat waves to absolute deluges. It's all quite exciting really.
On a more professional note, I've found myself thinking a lot recently about how fixated we in the localization industry can be on translation production specifically. My mind turns to how many times I've seen various other LSPs espouse the high-quality output of their MT (whether NMT or LLM-powered), and how this will improve translation outcomes, expedite project management, and enable fully scalable language quality evaluation processes (more on that from us shortly).
There is a need, as I've heard so many times at industry events over the course of this year, for us to start looking at the bigger picture. As localization providers, our job isn't just to translate content (although that is a big part of it). Instead, we have a responsibility to help guide clients, ensuring their localization programs are set up for true success.
As such, at Alpha, we're constantly looking at new ways to incorporate technology beyond the translation ecosystem itself. Machine translation and AI allow us to translate more quickly and at lower costs than ever before - but is that really where the story ends? No - we say it is vital to look beyond: with the data now available at our fingertips, as well as the raw heft of AI, can we not explore more creative approaches to localization programming? Think progressive rounds of localization, depending on content performance: an initial MT sweep of your resources, with pillar posts picked out to be reviewed by humans once they're deemed popular enough.
Improve your localization strategy with AI and data analytics
Modern AI models, whether powered by neural machine translation (NMT) or LLM, can generate multiple localized variants of the same content in much shorter turnaround times than previously expected. These variants can be differentiated across formality, style, or inclusion of local cultural references. They can even be customized to emulate your tone of voice through LLM fine-tuning or customized MT engine training.
By leveraging this capability correctly, businesses can deploy the same level of experimentation in localized content as they would with the source. Just think of the possibilities:
Experiment with messaging at scale: Instead of simply taking one translation and running with it, companies can deploy a spectrum of localized content tailored to different segments within a market.
Respond to real-time trends: AI can quickly adapt content to reflect emerging local events, memes, or preferences, keeping brands relevant.
Of course, here we see AI not as the whole value-add, but as part of a wider network that can unlock the real power of your localization processes. Talk to your marketing teams about which of their metrics can be used to identify content potential for international markets - focus particularly on where each piece of content sits and its intended goal to see whether you should be looking at conversion, engagement, or satisfaction metrics.
Maintaining a strong brand TOV in the age of AI content production
The latest blog from our lead copywriter, Amelia Morrey, looks at how brands can deliver personality even when trying to leverage AI in their content production pipeline.
Brand inconsistency is nothing new - heck, people are often the worst culprits of this, especially when a clear identity hasn't been established. So why are we so worried about preserving brand identity using AI tools when teams have long needed to be corralled into being 'on-brand'?
Part of that is because of the pure nature of large-language models. As largely predictive machines, they trend towards an inevitable middle-ground, meaning that brands overly reliant on AI can begin to sound identical to their peers. Amelia argues that up-to-date style guides and brand guidelines are therefore only going to become more important as the content landscape evolves.
Protecting your brand doesn't mean avoiding AI completely - especially not when attention spans are dwindling and content needs balloon. Instead, we need to apply these tools with careful supervision from a human team that understands what makes you, well, you.
Find out more about avoiding robotic outputs in Amelia's article.
Knowledge graphs: The next frontier in AI-powered business intelligence
It will come as no surprise that artificial intelligence, and generative AI specifically, is continuing to have a profound impact on how businesses process and leverage their information. That said, critical limitations in how traditional AI systems understand context and relationships across complex data are becoming evident. While the pairing of LLMs and retrieval-augmented generation (RAG) has marked a significant step forward, this too has its limits. GraphRAG, powered by knowledge graphs, presents a quantum leap in AI capability that businesses on the cutting edge cannot afford to ignore.
Traditional RAG systems retrieve information through vector similarity searches, essentially matching text fragments based on semantic similarity. While functional, this approach suffers from what our Head of AI Development, Ken Ming Chang, identifies as “fragmented context awareness.” The system treats each piece of information as an isolated fragment, missing the crucial connections that enable sophisticated reasoning and comprehensive understanding.
GraphRAG transforms this paradigm by introducing knowledge graphs, sophisticated data structures that map entities, relationships, and connections in a highly organized, relational framework. If that sounds a little overly complex, think of it as something akin to a minutely detailed and extensive mind map, in which all relationships are also labelled. Instead of retrieving disconnected text chunks, GraphRAG navigates through interconnected data, enabling multi-hop reasoning and extracting deeper insights from complex information networks.
As the adage goes: seeing is believing. In order to demonstrate the massive leap in reasoning quality available through migration from RAG to GraphRAG, Ken set up a little experiment.
And there you have it! Another month of updates from Alpha CRC over and done. I hope you've enjoyed July's monthly Dispatch. We look forward to catching you again next month.
Jack Simpson
Marketing Manager
Did this issue leave you wanting more, more, more? (Little nod to the icon that is Andrea True)