Message Classification
Message classification is the process — usually AI-assisted — of assigning a category or type to a message after it's written, so downstream systems can route, prioritize and search it without a human tagging it by hand.
Classifying every message manually doesn't scale — nobody stops to tag a message "question" before hitting send. So classification is typically automated: a model reads the content and assigns a type, an urgency, and sometimes a "who actually needs this" recommendation.
The design question that matters most is when classification happens relative to sending. If it runs before send and can block or alter the message, it adds latency and friction to every single message — a cost paid on the most frequent operation in the whole system. If it runs after the message is already persisted and delivered, classification becomes enrichment: it can be wrong, slow, or briefly unavailable without anyone being unable to send.
Classification quality also matters for trust. A message misclassified as low-priority can get buried in a digest when it needed attention; one misclassified as urgent adds to fatigue. That's why classification is usually paired with an easy human override — the AI's guess is a starting point, not a verdict.
Message Classification, in the product
Aanty classifies every message asynchronously after it's persisted — never on the send path — assigning type, urgency and a recommended "who needs this" list. The author can always override the type, and classification never delays or blocks send.
Related terms and pages
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