Evidence · first-party tested/Best AI Meeting Notetakers for Accurate Transcripts, Summaries, and Action Items
AI chat answers a meeting question with a grounded transcript-backed response, returning that the call was scheduled for '6th August' and explicitly indicating it is reading the transcription.
What was measured
Chat with Notes / Ask Questions
Gives grounded answers with the cited moment and admits when unknown.
context, not decisivetransformation
Q&A over notes is useful, but it is an add-on to the capture and summarization job rather than a core measure of it. (3 of 3 judges)
What was given, what came back
Test input: AI Demos Daily Standup — 31 July 2026 · image · group: ai-meeting-notetaker
Input — what we sent
AI Demos Daily Standup — 31 July 2026
A real 25-minute technical engineering daily standup with 14 attendees and about 10 active speakers, used as the single parallel-capture meeting for evaluating AI meeting notetakers on transcription, diarization, summaries, action items, search/chat, and collaboration features.
Why this input is hard
- · Transcription accuracy for real names, tool names, numbers, and technical jargon
- · Speaker diarization across multiple active speakers
- · Robustness to overlapping speech, crosstalk, and rapid turn-taking
- · Join reliability for bot-based and botless capture
- · Summary quality on identical source material
- · Action-item extraction with correct owners and commitments
- · Topic segmentation of standup updates
- · Search and chat grounded in the meeting content
- · Sharing, API, MCP, integrations, plan limits, languages, and privacy feature coverage
Output — unretouched
Also checked on this input — same tool, 8 other criteria
Action-Item Extraction◐ MixedIt extracted the real action item about updating logs, but owner attribution was wrong because the misheard name cascaded into the action item and showed 'Nadine' instead of 'Mahreen.'Join Method & Reliability✓ WorkedThe bot joined the Google Meet call and stayed connected through the full meeting capture with no visible dropout or mid-call disconnection.Search Across Notes⚠ StruggledThere is no dedicated transcript search UI, so direct keyword lookup across notes is not available from the transcript view and is only routed indirectly through AI Chat.Speaker Diarization◐ MixedIt identified most speakers, but the report says multiple transcript lines were assigned to the wrong speaker, so speaker-to-statement mapping was not fully reliable across transitions.Summary Quality✓ WorkedThe meeting summary is structured into skimmable topic sections rather than one blob, with headings like 'Tool testing & publishing' and 'Access, tracker & expiries.'Summary Quality◐ MixedThe summary can hallucinate a person name: the report says it substituted 'Nadine' for 'Mahreen' in a summary bullet, creating a false team-member attribution.Topic Segmentation✓ WorkedIt breaks the standup into logical topic sections that reflect meeting flow, instead of presenting the notes as a single undifferentiated block.Transcription Accuracy◐ MixedOn this ~25-minute multi-speaker standup, HappyScribe captured the vast majority of names, tools, and jargon correctly, and the report records only 1–2 misheard words.
Provenance
- Observation
- 09e56c77-5be8-4485-90bc-76a453f3197a
- Evidence run
- ace58582-3d1e-48ee-996c-9b3cd03f27a2
- Study
- AI Meeting Notetakers — Capture Accurate Transcripts, Summaries & Action Items From Live Calls
- Research task
- 86baxegnv
- Tested at
- not recorded
- Source
- first-party
- Evidence state
- verified
- Proof shown
- input + output shown
- Cost / latency
- not captured
- Repeat run
- not captured
- Tester
- not captured
The last three rows are honest blanks, not placeholders — our capture has no field for them yet.
Query this
get_evidence({
tool: "happyscribe",
scenario: "ai-meeting-notetaker"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 7 other tools
measured on Chat with Notes / Ask Questions
Fathom✓ WorkedAsk Fathom answers direct factual questions from the meeting notes with grounded references; for one query it answered that a call was scheduled for 6th August and linked the supporting transcript mention.Fellow✓ WorkedAsk Fellow returned grounded answers to natural-language questions against the meeting notes, and the tested query produced a cited response rather than an unsupported hallucination.Fireflies.ai✓ WorkedAskFred answered a natural-language question with a specific grounded response ('August 6th') and relevant context, with no hallucination reported in the tested query.Granola✓ WorkedThe chat/Q&A surface gives grounded answers from the meeting record: on the tool-access question it says access was confirmed that day, cites both the notes and transcript, and identifies rerunning testing as the next step.MeetGeek◐ MixedIt answers direct grounded questions correctly, but the report records an incorrect answer on a speaker-dependent scheduling question, so chat is reliable for simple queries but weaker when attribution/context matters.Notta✓ WorkedThe Q&A interface answered a natural-language question with a grounded response from the meeting record, including the specific date "6th August," and the report observed no hallucinations.Otter.ai✓ WorkedOtter’s AI Chat answered meeting questions with a grounded response and a specific timestamp, and the report says the answers were cited and free of hallucinations in the tested queries.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com