Evidence · first-party tested/Best AI Meeting Notetakers for Accurate Transcripts, Summaries, and Action Items
Attributed consecutive turns to distinct speakers in the transcript, and the report says speaker identification was almost complete with only minor attribution errors.
What was measured
Speaker Diarization
Correctly attributes who said what across a multi-speaker standup.
decisive for this rankingtransformation
Correctly attributing who said what is part of making the transcript and notes trustworthy in multi-speaker meetings. (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, 7 other criteria
Action-Item Extraction✓ WorkedGrouped action items by owner, attributed them to the correct team member, and exposed a clickable source timestamp (19:13) for at least one item.Chat with Notes / Ask Questions✓ WorkedAskFred answered a natural-language question with a specific grounded response ('August 6th') and relevant context, with no hallucination reported in the tested query.Join Method & Reliability✓ WorkedUsed a bot-based Google Meet join: the Fireflies notetaker appeared in the People panel, and the recording player showed it still present in a 23:40 capture, matching the report’s claim of uninterrupted full-call capture.Search Across Notes✓ WorkedTranscript search worked with exact-match retrieval: a Ctrl+F query for 'API' returned 1/1 match at 20:42 with the hit highlighted and a clickable timestamp.Summary Quality✓ WorkedProduced a structured notes summary with a named header ('Task Status and Issue Resolution') rather than a blob, and the report says the full summary was multi-section and did not drop important points.Topic Segmentation✓ WorkedBroke the meeting notes into named sections with descriptive headers and short recap paragraphs, making the output skimmable instead of one undifferentiated block.Transcription Accuracy✓ WorkedGenerated a timestamped transcript view, and the report says the full transcript was very accurate: nearly all names, tools, jargon, and numbers were captured correctly with no significant misheard terms or hallucinations.
Provenance
- Observation
- a1eae6d9-4eff-41c0-9357-c4b9c8e6f7fb
- 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: "fireflies-ai",
scenario: "ai-meeting-notetaker"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 7 other tools
measured on Speaker Diarization
Fathom◐ MixedFathom separates most speakers correctly in a busy multi-speaker standup, but the report observed one rapid-transition segment where two speakers' lines were merged into a single speaker block.Fellow✓ WorkedThe transcript attributed speaker turns correctly across the standup, with all ~10 speakers labeled by name and no attribution errors or generic labels reported.Granola✗ FailedGranola’s default capture does not attribute speakers: the settings panel shows Speaker tags switched off, and the transcript excerpt is a plain text wall with no speaker labels, so diarization is absent unless the user manually enables it.HappyScribe◐ 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.MeetGeek◐ MixedIt identifies most speakers in a multi-speaker standup, but leaves at least one utterance as "Unknown speaker" and misattributes some lines to the wrong speaker, so attribution is not fully reliable.Notta⚠ StruggledThe transcript contained a line labeled with another notetaker’s name (HappyScribe), which indicates cross-tool contamination or labeling error and breaks speaker attribution for that segment.Otter.ai✗ FailedOtter’s diarization was effectively unusable in this multi-speaker standup: only 1 of about 10 active speakers was identified by name, while the other 9 were left as generic labels or unattributed, which the report summarizes as a 90% failure rate.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com