Evidence · first-party tested/Best AI Meeting Notetakers for Accurate Transcripts, Summaries, and Action Items
It transcribes a normal ~25-minute, ~10-active-speaker engineering standup mostly accurately, with only minor proper-noun/term drift noted in the report; one example given is "Madin" being misheard for "Mahreen".
What was measured
Transcription Accuracy
Word accuracy on the shared call, especially names, tools, numbers, and jargon.
decisive for this rankingtransformation
If the transcript gets names, numbers, and jargon wrong, the note-taker has failed at the core job of capturing the call accurately. (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✓ WorkedIt extracts real commitments as action items rather than noise; the report says all extracted items had correct ownership and timing, and the visible note includes an owned action item with timestamp 19:51.Chat with Notes / Ask Questions◐ 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.Editability✓ WorkedUsers can edit generated outputs inline before sharing; the report says summary, action items, and the full transcript are all editable, and the UI shows editable summary text.Join Method & Reliability✓ WorkedThe bot successfully joined a Google Meet call and the report says it captured the full ~30-minute meeting with zero disconnections or data loss.Search Across Notes✓ WorkedIt supports transcript search with precise retrieval: searching for "api" surfaces the matching text in context and the report says timestamps are returned to within a few seconds.Speaker Diarization◐ 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.Summary Quality✓ WorkedIt produces a clear, skimmable meeting summary with topic organization and a Next Steps section, and the report says it preserved the major decisions and discussion points.Topic Segmentation✓ WorkedIt breaks the meeting into useful numbered topic sections instead of one blob, with a visible hierarchy under "Topics & Highlights" and the report also noting an Insights tab alongside the segmentation.
Provenance
- Observation
- 5a191ed7-02a9-4979-931d-9219e0b75fce
- 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: "meetgeek",
scenario: "ai-meeting-notetaker"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 7 other tools
measured on Transcription Accuracy
Fathom◐ MixedFathom's transcript mostly preserves the meeting's names and technical content, but the report records one confirmed name-level error: "Mahreen" was rendered as "Meryl."Fellow✓ WorkedThe transcript was near-clean: the tool captured nearly all names, technical jargon, and numbers correctly, with no significant misheard terms or hallucinations observed in the tested meeting.Fireflies.ai✓ 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.Granola✗ FailedOn this 25-minute, multi-speaker standup, Granola’s transcript quality is unreliable: the published excerpt shows garbled phrasing and mistranscribed wording, and the report says the mishearing pattern recurs across early, middle, and late sections rather than being isolated to one moment.HappyScribe◐ 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.Notta✓ WorkedNotta’s transcript capture was accurate on the evaluated standup: the report says it correctly captured names, tool names, numbers, and engineering jargon with no significant word-level errors, silent hallucinations, or misheard terms.Otter.ai✓ WorkedOtter generated a full transcript for the standup and, per the report, captured names, tool names, jargon, and numbers correctly with minimal errors, making the transcript reliable for reference.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com