Maya Chen - Client
45:12
Q
Did the client agree to the new pricing model?
Partially. Maya accepted annual tiers1, but flagged month-to-month pricing as a blocker for procurement2. David asked to revisit after Q3 budgeting3.
Product · Evidence Q&A
A copilot that refuses to answer without evidence.
If the recording doesn’t support the claim, Evidence Q&A doesn’t make the claim. Every response is grounded in a specific moment - quote, speaker, timestamp, frame - and you can click any citation to jump straight to it.
The shape of an answer
What a cited answer actually looks like.
A working example, not a screenshot. The question above is real. So are the timestamps, the speakers, and the quotes underneath. The same shape ships from every Citesvue recording.
Why this design exists
Hallucination is the default. We built around it.
Most LLMs produce answers first and justify them second. Evidence Q&A inverts that: the system retrieves grounded transcript segments and frame captures first, then constrains the answer to what they support.
If the evidence is thin, the copilot says so - and shows the thinnest spot, not a confident fabrication. This is the difference between a useful tool in a regulated meeting and a liability.
The pipeline
How a question becomes an answer.
- 01
Parse
Query is parsed and expanded - synonyms, speaker references, time expressions.
- 02
Retrieve
Transcript and frame index returned with confidence scoring on each segment.
- 03
Triage
Low-confidence evidence is flagged rather than silently dropped.
- 04
Compose
Answer is built only from retrieved content - never beyond it.
- 05
Cite
Every claim binds to one or more citations; the UI renders them inline.
Question shapes
What the copilot can answer.
Factual
- “What version were we testing?”
- “Who approved the scope change?”
- “What error did the payment page throw?”
Synthetic
- “Summarise every objection raised during the demo.”
- “What did Maya actually decide vs. defer?”
- “Which bugs did the client call critical?”
Comparative
- “How does this session’s feedback differ from last week’s?”
- “Did anyone contradict the architect on the auth design?”
- “What changed between cycle 4 and cycle 5?”
Multi-turn memory
Follow-ups work - every turn still cites.
Ask “what else did Maya flag as a blocker?” and the copilot tracks the referent without re-asking who Maya is or which session you meant. Conversation memory never replaces evidence grounding - every turn carries its own citations.
Citation mode
A brief, not a chat.
One-click switch to a brief format: numbered claims, an evidence appendix, exportable as PDF or DOCX. Designed for QA sign-off, client deliverables, audit responses, and any conversation where “prove it” is a real question.
Built-in honesty
Four things the copilot does instead of guessing.
Declines
“The recording doesn’t cover this.”
Hedges
“Maya implied this around 31:20 but did not confirm.”
Ranks
Confidence score surfaced on every answer.
Dissents
Flags speaker disagreement instead of averaging it out.
Where it earns its keep
Use cases that depend on this.
Built for moments when the answer must hold up under scrutiny.
Scenario · 01PM
UAT sign-off
A PM needs to prove the client approved scope changes before shipping. Cited answers carry the moment they were spoken.
Outcome · Sign-off in minutes, with evidence attached.
Scenario · 02AE
Sales QBR prep
An AE pulls every objection the champion raised in the last three calls - across recordings, not from memory.
Outcome · Faster prep, fewer surprises in the room.
Scenario · 03Eng
Incident retros
A staff engineer reconstructs the decision sequence from a three-hour war room recording.
Outcome · Clear, defensible incident timeline.
Scenario · 04Research
Cross-session synthesis
A researcher asks one question across eight interviews and gets a per-interview citation trail back.
Outcome · Synthesis with sources, not vibes.
Limits, on purpose
What the copilot will not do.
- No speculation
beyond the evidence.
- No training
on your recordings.
- No answers
without at least one citation.
- No silent downgrade
when evidence is weak - say so instead.
Common questions
What enterprise buyers ask about Evidence Q&A.
- No. Customer recordings and the structured artifacts derived from them are never used to train the underlying models. Ever.
- Retrieval-first architecture: the system fetches grounded transcript segments and frame captures before composing anything. Low-support answers are declined or hedged, not fabricated.
- Yes. Workspace and project scope, speaker filters, date ranges, and integration-source filters all narrow the retrieval window.
- BYOK is on the Enterprise track - bring your own OpenAI, Anthropic, or Bedrock key. Talk to sales to scope.
- It stays in your workspace so playback keeps working, counting against plan storage. Deleting a recording removes the media and the queryable index - transcript, frames, artifacts - together.
- Not yet. Q&A lives in the product today; a public API with the same cited response shape is on the roadmap.
Continue exploring
What grounds - and consumes - the same index.
Closing argumentYour next recording could be
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.