Session replay captures what happened. A video scanner makes selected behaviors queryable: you define an observable pattern, run it against a recording, and receive typed observations tied to exact evidence ranges.
The scanner workflow
- Create a saved
monitor,classifier, orfind_momentsscanner. - Export an authorized replay recording and complete a private upload.
- Start one run with its ready
upload_idand attach metadata such as workspace, cohort, or user ID. - Poll the run, then retrieve its observations or query observations across runs.
POST /v1/scanners/{scanner_id}/runs
{
"upload_id": "upl_YOUR_READY_UPLOAD",
"metadata": {"workspace": "acme", "cohort": "new-user"}
}Write for visible evidence
A useful scanner instruction describes what can be seen. “Find users who dislike onboarding” presumes an internal state. “Find moments where the user repeats the same setup action, receives a visible error, or leaves without advancing” defines reviewable evidence. Speech-only evidence is outside this first scanner version.
A supported monitor no-match is one explicit boolean false observation. A completed zero-observation run is unanswerable from sampled frames, not false and not a processing failure.
Treat observations as evidence records
Every observation names its semantics and value type, carries a relevance score, and points back to its run, scanner version, analysis, source metadata, and timestamped evidence. Relevance is a ranking signal—not calibrated statistical confidence.
How this relates to replay-native AI
Replay-native products can search recordings already inside their analytics system and may add cohorts, alerts, and continuous scanning. ChatClipThat is a general recorded-video intelligence API: you privately upload the authorized video and control the surrounding metadata. Scanner runs return JSON and never render. If you need continuous ingestion today, orchestrate exports, uploads, and run creation in your own service.