Nine artifact types. One evidence chain each.
A summary forgets. An artifact doesn’t. Citesvue extracts nine structured artifact types from every recording — each severity-graded, speaker-attributed, and carrying the quote, timestamp, and frame that produced it.
The nine types, mapped.
One row per type. What triggers detection, the default severity model, and where the artifact tends to land.
Five required fields, always populated. Two optional enrichments.
Verbatim words that triggered the artifact.
Attributed segment-by-segment.
Click-to-jump in the recording.
Screen state at that moment.
Type, severity, structured sub-fields.
Two optional enrichments: suggested reproduction steps for bugs and suggested owner for action items — both labelled as suggestions, never asserted as fact.
How extraction actually works.
Transcript segments are scored against artifact-type signatures. Candidates are cross-checked against the visual layer — a bug candidate without an on-screen UI signal is downgraded; an approval candidate corroborated by screen context is promoted.
Severity is inferred from lexical and paralinguistic cues, then surfaced in the review queue for human override. The model is opinionated; the workflow is not.
Extraction is a draft, not a verdict.
Every artifact lands in a review queue where a teammate can approve, reject, edit, merge duplicates, or reassign type. Reviewed artifacts build a per-workspace signal that sharpens extraction over time. Nothing ships externally until reviewed — unless the workspace explicitly opts into auto-push.
A 52-minute UAT, end-to-end.
1 recording · 52 min · 3 participants
4 bugs · 6 requirements · 3 decisions · 2 risks · 9 action items
PM reviews queue, adjusts severity on 1 bug, assigns owners to 5 actions, pushes to Jira + Notion in under 4 minutes
The critical bug: a quote from the client at 31:08 about the export producing an empty CSV, paired with the frame showing the error toast, auto-classified critical based on the phrase “this is a blocker for us.”
Three lines that explain the difference.
- A summary compresses.
An artifact preserves.
- A summary is one paragraph.
Artifacts are nine structured tables.
- A summary dies in a doc.
Artifacts route into the systems where work happens.
Severity is a real object, not a vibe.
Every severity assignment exposes the reasoning signal that produced it — the phrase that triggered Critical, the screen context that corroborated it, the confidence score. Reviewers can override; overrides feed workspace-level calibration. Severity is auditable.
What artifact extraction actually changes.
Custom artifact types.
Workspaces will be able to define their own artifact types and extraction rules — for example a compliance obligation, customer commitment, or SLA breach. Tagged as coming, never promised by date.
What enterprise buyers ask about extraction.
- Every extraction surfaces a confidence score. The default workflow assumes human sign-off via the review queue before downstream push — extraction is a draft, not a verdict.
- Yes, per-workspace. Enable only the types your team actually triages.
- Yes — CSV, JSON, and full REST API access. Downstream tooling can consume artifacts without going through the UI.
- Yes, with the caveat that visual grounding (frames, OCR, on-screen evidence) is unavailable for those artifacts.
- Workspace-level redaction policies are applied before the artifact is written. Audit logs capture every read and export.
What feeds — and what consumes — extracted artifacts.
Your next recording could be
your most valuable asset.
Or it could sit in a Drive folder nobody opens again. The difference is whether it has citations attached.
- SetupOne drag-and-drop. No bots, no plugins.
- First insightCited Q&A on a 60-min recording in under 6 minutes.
- Cancel anytimeFull data export, full right to erasure.