You ran the UAT. Fourteen Jira tabs open. You’re scrubbing the recording for the exact second the dropdown broke, screenshotting, pasting, rewriting the repro steps from what you half-remember the user saying. That’s not testing — that’s typing. Citesvue does the typing.
“When I set the transaction limit above ten million, it silently rounds down to 9,999,999 — there's no warning, and the confirmation screen shows the correct number.”
Detected error · no validation surface. OCR confirms confirmation screen shows $10,000,000.00. Backend response body shows 9999999.
UAT session ends at 4pm. Real work starts at 4:01. Rewind for the error screen. Screenshot. Open Jira. Paste. Retype the user’s words from memory. Guess at severity. Repeat thirteen times. Half the bugs filed on Friday are logged without the actual quote that triggered them.
A bug isn’t “I saw a problem.” A bug is who hit it, what they said, what was on the screen, what they were trying to do, and whether it reproduces. Citesvue captures all five during the recording. Your job stops being stenography and starts being triage.
Session ends. You upload.
Citesvue extracts: 11 BUGS · 3 OPEN QUESTIONS · 2 ACTION ITEMS — each with severity, component, quote, frame, OCR error text, draft repro path.
You approve 9, revise 2 (tighten severity), push all 11 to Jira.
You close your laptop.
Quote + speaker + severity + timestamp + frame + OCR + draft repro steps. A complete Jira ticket — not a placeholder.
Across sprints — which bugs keep resurfacing, auto-grouped by component.
Every bug found, sortable by severity, with one-click jump to the exact moment in the recording.
Trained on UAT session patterns. Editable per workspace. Bulk-override for your team’s taxonomy.
The platform detects error screens (red text, modal dialogs, 404/500, console errors) automatically. OCR reads error messages into the ticket body.
Full evidence travels with the ticket — frame attached, quote in description, severity set, component tagged, reporter = session host.
Trends across releases. Recurring regressions surfaced. Which components break most often? Which user profiles hit them?
Issue status flows back into the artifact review state — close in Jira, close in Citesvue.
Bug documentation today takes 3–5× longer than the testing itself. A 1-hour UAT generates 3–4 hours of documentation. Citesvue collapses that to 10–15 minutes of review. A QA team running four UATs a week saves ~12 hours — roughly one and a half working days, per tester. The bugs filed are also more complete, which cuts downstream dev clarification time.
Native push with full evidence payload. Configurable project, issue type, custom fields.
Equal priority. Cycles, labels, priority mapping built in.
Per-channel session digest posted to the QA channel.
Issue push for infra-facing teams.
TestRail · Zephyr · custom bug trackers.
Release readiness reports with full evidence.
Session → 11 bugs → all in Jira before you leave the call.
Pull every BUG artifact from the last 8 sessions, filter by component.
The ticket has the quote, the frame, the OCR — the dev doesn’t need to ask “which button?”
Record yourself testing. Bugs extracted with no manual note-taking.
Branded Confluence export: every bug found, status, severity, citation.
“I have fourteen Jira tabs open. That’s just today.”
“The dev closed it as “can’t reproduce” and I had to rewatch 40 minutes of recording to prove it.”
“I know this bug. We filed it in sprint 18. Let me find it.”
Pro covers a single QA lead. Team unlocks shared workspace, project organisation by sprint or client, webhook access for TestRail/Zephyr. Enterprise for regulated industries needing on-prem, SSO, retention controls.
PMs frequently share UAT recordings with QA — REQUIREMENT artifacts come out alongside BUG artifacts.
When agencies own client UAT, branded findings packs ship from the same evidence.
The OCR + UI-state layer that powers complete bug evidence.
Or it could sit in a Drive folder nobody opens again. The difference is whether it has citations attached.