Every data pipeline starts the same way: a stream of raw records lands at the entrance, and the first thing you need is to figure out what each record actually is. What type, what format, what attributes, whether it's worth spending expensive downstream steps on. At this point, teams usually pick between two bad options: hand the work to a large language model — expensive and slow on every single record — or hardcode it into regexes and heuristics — cheap, but brittle and blind to meaning.
A third option appeared recently: System One models. I spent time with one of them, Jev from TypeSafe, and the conclusion I want to lock in is this: the most valuable place for such a model is not somewhere in the middle of the pipeline. It is at the very entrance, as a labeling and enrichment layer.
Continue reading “How to Put a System One Classifier at the Pipeline Entrance”