You ran the UAT. Citesvue creates transcript-grounded bugs first. When the screen is part of the proof, request visual evidence to add OCR and frames before you review and push the finding.
“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 starts with who hit it, what they said, and what they were trying to do. Transcript findings capture that immediately; optional visual evidence adds what was on screen when the screen is needed to prove it.
Session ends. You upload.
Transcript processing returns bugs, open questions, and action items with severity, verbatim quote, speaker, and timestamp.
You request visual evidence for this video; verified frames and OCR corroborate the screen-dependent bugs.
You approve 9, revise 2 (tighten severity), and push each one to Jira - typed as a Bug, severity mapped to priority.
You close your laptop.
Quote + speaker + severity + timestamp first; verified frame and OCR evidence are added when visual analysis is requested.
Every finding from every session in one view - filter by project and type, then export the lot as CSV.
Every bug found, sortable by severity, with one-click jump to the exact moment in the recording.
Inferred from what was said and what was on screen. Editable on every finding; bulk approve and reject in the review queue.
Request visual evidence for video when error screens matter. The verified generation can add modal text, 404/500 states, console errors, and OCR to the existing finding.
Connect over OAuth, pick the project or team, and push. A bug lands typed as a Bug with severity mapped to priority and the quote in the description - and pushing it again updates the existing issue instead of duplicating it.
One click on the catalogue or on a single session: id, title, type, severity, status, and the evidence quote, ready for the release spreadsheet.
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.
OAuth connect with a live project picker. Bugs arrive typed as Bugs, severity mapped to priority, evidence quote in the body.
OAuth connect with a live team picker. Findings arrive as issues with severity mapped to Linear priority.
Findings and session recaps posted to your QA channel, evidence quote included.
Release notes and findings databases with cited evidence.
HMAC-signed JSON into custom bug trackers and pipelines.
Where Google Workspace export is enabled for the connected workspace, deliver selected content into your own Google account. CSV export remains available for the spreadsheet workflow.
Session → 11 bugs → pushed to 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.
Export to PDF or Word: 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 (40 recordings, 10 optional visual-evidence hours a month, Jira / Linear / Notion / Slack push). Team gives every seat 60 recordings and 20 visual-evidence hours with a shared workspace from 3 seats. Transcript-first content does not use those hours.
PMs frequently share UAT recordings with QA - REQUIREMENT artifacts come out alongside BUG artifacts.
When agencies own client UAT, cited findings packs ship from the same evidence.
The OCR + UI-state layer that powers complete bug evidence.
The standard behind this workflow: evidence tests, defect records, sign-off packet.
The ticket format that ends the cannot-reproduce loop.
Or it could sit in a Drive folder nobody opens again. The difference is whether it has citations attached.
