How smart summaries work
Smart summaries are generated after Dailybot has collected check-in responses for a given run (or a window you select in the product). A model reads the text answers — not private credentials or messages outside the check-in — and returns a compact narrative: what most people worked on, what repeats across the team, which blockers or risks appear, and what looks like a concrete next step.
Where summaries appear
Placement depends on how your check-in is configured. Typical locations include the compiled report posted to a team channel, a summary block on the web check-in detail view, and exports or digests if your org uses them. If smart summaries are disabled for the check-in or your plan does not include the feature, you see only the standard chronological report without the AI paragraph.
Accuracy
Summaries reflect the text people submitted. They can miss sarcasm, understate urgency, or merge distinct threads if answers are vague. They do not invent facts: if nobody mentioned a dependency, the summary should not claim one. Always treat the summary as a starting point for reading individual responses, not a substitute for them.
Limitations
- Summaries are only as good as the input; one-word answers produce thin output.
- Language and formatting quirks (pasted logs, heavy jargon) can confuse thematic grouping.
- Regulatory or contractual review of AI output is your responsibility; do not use summaries as the sole record for compliance without human verification.