The Problem We Were Actually Solving
I still remember the call from our lead developer in Douala, Cameroon, who told us that the language model we'd deployed on a popular platform store wasn't working for a certain class of users. In Cameroon, a platform that works is one that uses the official language, French, or the lingua franca, pidgin English. Not the platform's dominant language, English. The platform's API, built with an offshore team in India, didn't account for the differences in languages and character sets our users in Cameroon faced every day. Our users couldn't even see the available models, let alone use them. We were pushing an AI dream onto a community that had no choice but to adapt to a system that wasn't built for them.
What We Tried First (And Why It Failed)
We initially thought that building our own APIs on the platform's infrastructure would solve the problem. We assumed that our local team could just use the same language models and adapt the platform APIs to French and pidgin. The API calls were more or less the same, we figured. We even convinced our offshore team to help us. After a few weeks of development, we realized we'd made a critical misjudgment: the differences in languages and character sets were not just about user interface but also about fundamental system architecture. The platform's model hosting and deployment required a specific ecosystem that didn't exist outside the platform's environment. Our adapted API still wouldn't work. Every request in our French and pidgin interfaces failed to return any results, and we received error messages about encoding issues we didn't understand. We'd taken a shortcut that didn't exist in reality.
The Architecture Decision
We eventually realized that we needed to rethink our entire approach to global access. We couldn't just adapt the platform's APIs; we needed to create an entirely new architecture that catered to local languages and character sets. We had to build a custom storage system and an interface that could handle the differences in language and encoding that our users faced every day. We brought in a local team that specialized in African languages, and together, we created a new API that utilized local storage and translation layers. We even developed a small set of language models that were trained on pidgin English and French data. This new architecture not only solved our problem but also helped us tap into a more diverse and global user base.
What The Numbers Said After
After deploying our new architecture, we saw a significant drop in errors related to encoding and language incompatibility. Our French and pidgin interfaces worked seamlessly, and our users were able to see and use the language models we'd deployed for them. Our metrics showed a 90% reduction in failed requests, and our user satisfaction ratings increased by 30%. We were no longer pushing a dream onto a community that had no choice but to adapt; we were delivering a product that truly served the needs of our users in Cameroon and beyond.
What I Would Do Differently
In hindsight, I would have engaged the local team and our offshore team earlier in the development process to ensure that everyone understood the complexity of our problem. I would have invested more time and resources into learning about the languages and character sets our users faced every day. I also would have explored alternative architectures from the start, considering a custom solution from the beginning. Our journey taught me the importance of humility in the face of global complexity and the value of collaboration when tackling seemingly insurmountable problems. By listening to our users in Cameroon, we were able to build a system that truly served their needs.
Top comments (0)