The Model Was Never the Bottleneck: What Shipping a Text Classifier Into a Government Office Taught
Disclosure: I designed and led the build of the system described here as CTO of the company that developed it, and I have a commercial interest in the product. The research write-up is my own work and is available as a preprint (arXiv:2604.03672).
TL;DR We built a classifier that sorts incoming citizen appeals into complaints, applications and proposals, and routes them to the right department. BERT was the most accurate model we tested. We shipped a smaller Word2Vec+LSTM instead. Then load testing showed that neither choice mattered much, because the queue was never in the GPU. It was in the five people doing review.
The job
A government office receives written appeals from citizens. Somebody has to read each one, decide what kind of appeal it is, decide which department owns it, register it, and start the clock, because the response deadline is set by law.
When we...
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