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Let TimeCampus name the project behind each captured hour
A keyword classifier runs first, an LLM refines it, and neither one is allowed to change your timesheet

Related scenarios
AI Native
Weekly suggestions that will never tell you to work more
TimeCampus already runs a weekly pass over your own tracked time and the balance targets you set, and produces a short list of suggestions — no unbroken deep-work block this week, focus ratio down against last week, a life domain sitting well below the share you asked for, time drifting from what you said matters. Each one carries the metrics it was derived from, so you can see exactly why it fired, and each can be accepted, snoozed or dismissed. The invariant that shapes the whole engine: any candidate whose net effect is more work is dropped before it is ever rendered.
AI Native
Insights that show their evidence — and admit what they missed
A productivity number you cannot interrogate is a number you should not act on. The scoring already built into TimeCampus reports its own coverage alongside its result, returns no score at all rather than a fabricated one when you have declared nothing to align against, and tags every work-pattern insight as positive, neutral, a warning or a recommendation with the observation behind it. Classification carries the same discipline: each categorised event records whether a rule, a model, or you produced the label.
AI Native
Find the hours that quietly leaked out of your week
TimeCampus already buckets the time that went nowhere — the waiting and commuting you logged, activity it could not categorise, and the unaccounted gaps between tracked work — and reports how much of it is genuinely reclaimable. The honest part is what it refuses to count: sleep is never a bucket, and anything you marked as deliberately chosen is excluded from the reclaimable total, so rest is never rebranded as waste. What surfaces is a small number of real hours you can decide to spend differently.


