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.
