Digs into saved full_pipeline_runs reports so you can see exactly why matched contacts did or didn't
end up with a queued message — per organization, per run, or aggregated across many runs.
Run new pipelines from Full Pipeline.
| Started | Group | Scan | Status | Orgs | Scanned | Matched | Messages | Conv % | Cost (self-reported) | Cost (ledger, recomputed) |
|---|---|---|---|---|---|---|---|---|---|---|
| Loading runs… | ||||||||||
The run-level cost breakdown below only covers what this pipeline's own ledger captured. This audit instead pulls
every actor run straight from Apify's account (GET /v2/actor-runs), looks up each run's real
result count from its dataset (GET /v2/datasets/:id), and computes cost_per_result = usageTotalUsd / result_count
per actor — so nothing run outside this app's attribution, or missed by the formula-based estimates, gets left out.
Rolls up every run matching the filters above — use this to spot systemic problems (e.g. one reason bucket dominating drops) rather than one-off run issues.