Evidence · first-party tested/Best AI Meeting Notetakers for Accurate Transcripts, Summaries, and Action Items
Otter 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.
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, 9 other criteria
Action-Item Extraction⚠ StruggledOtter extracted action items, including at least one due-today API-related task with an assignee, but the report says most items were left without an owner and duplicate entries also appeared, so the output needed manual cleanup before delegation.Chat with Notes / Ask Questions✓ 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.Editability✓ WorkedOtter exposes inline editing controls for transcript and summary outputs before sharing, so wrong content can be corrected in-product rather than only exported as-is.Join Method & Reliability✓ WorkedOtter’s bot joined Google Meet successfully and stayed connected through the full ~25-minute call with no mid-call dropout or ejection, so the meeting was captured end to end.Search Across Notes◐ MixedOtter does not show a direct transcript keyword-search workflow in the transcript view; lookup is routed through AI Chat instead, where a natural-language timestamp question returned a specific answer at 0:07:06.Sharing Without Registration✓ WorkedA shared meeting transcript opened without requiring sign-in, exposing the meeting title, metadata, and transcript snippets; the share dialog also offers restricted access and link-copy controls.Speaker Diarization✗ 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.Summary Quality✓ WorkedOtter produced a clear, structured meeting summary that the report says covered the key decisions and discussion points without dropping anything important, and the summary page loaded with organized sections like Overview and Action Items.Topic Segmentation✓ WorkedOtter broke the standup into useful topic sections rather than one blob, with named headings such as Issue Task Assignments and Status Updates and Error Resolution and Task Link Sharing, making the summary skimmable.
Provenance
- Observation
- 0efa5738-c840-44ba-a72a-b35d289e70fb
- 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: "otter-ai",
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.MeetGeek✓ WorkedIt 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".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.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com