Reads raw support messages and classifies each into bug, feature request, urgent, or noise. Deterministic rules. No ML. No data leaves your machine.
Support inboxes are where product signal goes to die.
Every day, users report bugs, ask for features, and flag urgent issues - buried in email threads, chat logs, and form submissions. By the time a human reads them, the signal is stale and the context is gone.
Manual triage is slow, inconsistent, and unrepeatable.
One person reads everything and decides what matters. Another person would decide differently. The same message gets different treatment on different days.
Feed it raw support messages. Get back a structured classification for each one:
Something is broken; a user cannot complete a task.
User asks for a capability that does not exist.
Revenue or security impact; needs human attention now.
Spam, duplicate, or not actionable.
$ echo '{"messages":[{"id":"m1","text":"The export button is broken"}]}' | python triage.py
[{"id": "m1", "bucket": "bug", "confidence": 0.9, "summary": "Classified as bug"}]
Deterministic keyword rules. No model, no API call, no data leaves your machine. Same input to same output, every time.
An LLM classifier is a black box you cannot audit.
When a classifier says "this is a bug", you cannot ask why, you cannot reproduce it, and you cannot fix it when it is wrong. A rule you can read is a rule you can trust.
EUR49 one-time. Runs locally. No subscription, no cloud, no data collection.
Buy now - EUR49