What refreshes when
What “daily” actually means
Each daily run produces one data point per prompt per tracked platform. Dashboard metrics for any given day are computed from the aggregated results of that day’s run, not from any single response. Week-over-week and month-over-month trends are built from the series of daily aggregates. A single day’s movement can still be noisy because LLM responses are inherently non-deterministic. For trend decisions, two-week moving averages are more reliable than single-day snapshots — see data freshness for guidance on how long to wait before reacting.Why not real-time
Three practical reasons rule out real-time monitoring: Cost. Measuring at the prompt-per-minute level across multiple AI platforms would be prohibitively expensive at any meaningful brand scale. Non-determinism. Single-run measurements are noisy. Daily aggregation across multiple runs produces more stable metrics than a continuous stream of single-point readings. Platforms themselves update slowly. The underlying training data and retrieval behavior of AI platforms changes on multi-week cycles. Faster sampling on Cited’s side does not produce faster signal — the bottleneck is the platforms, not the dashboard.Data availability timing
After the overnight pipeline completes, updated dashboard data is available within minutes. Brands checking their dashboard in the morning see the previous day’s data — no multi-day lag between collection and dashboard availability.Related concepts
- Non-determinism — why daily aggregation matters
- Data freshness — how to interpret confidence in the data
- How we generate prompts — what prompts the pipeline runs
- Impact scoring — how task priority is recomputed each cycle
Frequently asked questions
Why don't Tasks and the AI Narrative refresh every day like metrics do?
Why don't Tasks and the AI Narrative refresh every day like metrics do?
Because day-over-day movements in AI visibility are noisy. Regenerating Tasks or the Narrative every day would produce a churning task list that mostly reflects non-determinism, not real change. Weekly refreshes give the signal time to stabilize so the actions you take are based on directional movement, not single-day fluctuation.
What happens if a pipeline run fails?
What happens if a pipeline run fails?
Individual prompt failures are retried automatically. If a full run fails, the previous day’s data persists in your dashboard until the next successful run, and operational alerts fire to the Cited team. You will not see stale-versus-fresh data mixed in the same view.