Seven artifact types. One evidence chain each.
A summary forgets. An artifact doesn’t. Citesvue extracts seven structured artifact types from every recording - each severity-graded, speaker-attributed, and carrying the quote, timestamp, and frame that produced it.
The seven types, mapped.
One row per type. What triggers detection, the default severity model, and where the artifact tends to land.
Scroll to see every column →
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; a decision 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 seven 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.
- Extraction is a draft, not a verdict. The review workflow lets your team mark artifacts as reviewed before they get pushed or exported.
- Yes - JSON, CSV, PDF, and Word export, or straight into your own Google Sheets and Docs. A public API is on the roadmap.
- Yes, with the caveat that visual grounding (frames, OCR, on-screen evidence) is unavailable for those artifacts.
- Artifacts stay inside your workspace, scoped to your account at every query. Automated redaction policies are on the roadmap; today, review before push is the control.
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 upload, or send the notetaker. No plugins.
- First insightCited Q&A on a 60-min recording in under 6 minutes.
- Cancel anytimeFull data export, full right to erasure.