AI Text Classifier & Auto-Tagger

Zero-shot classifiers are genuinely good at this — sourced research puts accuracy at 85-95% for clearly separable categories — but accuracy quietly depends on how you name your labels, and most tools never tell you that. This one does: if your category names are too vague or overlapping to classify with real confidence, it says so explicitly instead of returning a silent low-confidence guess dressed up as a clean answer. Given categories "Bug report, Feature request, Praise" and a batch that includes "the export button is slow and honestly it'd be nice if it also supported CSV," the output doesn't force that line into one bucket — it identifies the line as split between Bug report and Feature request, quoting "the export button is slow" and "it'd be nice if it also supported CSV" as the two signals driving that split. That's the actual behavior you want from a classifier working on real, messy text, where items often aren't a clean fit for exactly one label. Sourced research also found that more distinct, well-defined category names measurably improve accuracy (about 5% in one study) — a genuinely useful thing to know before you type your labels in, not just a disclaimer buried in a FAQ. It's built for triaging batches fast: support tickets into a handful of real categories, survey responses into themes, feedback into whatever taxonomy actually matters to your team, not a fixed one a vendor picked for you. Because it's zero-shot, there's no training step and no minimum batch size — a genuinely different tradeoff than a classifier trained on labeled examples, useful precisely because most teams don't have a labeled dataset sitting around for a one-off sort. Change the category list and re-run the same batch for a different taxonomy in seconds, instead of retraining anything. Built by EngraveOcean as part of a small, growing set of free AI utilities.