{"observation":{"id":"089aaaff-2da4-4739-8045-46df7a2f1e6b","tool":"notta","tool_name":"Notta","criterion":"speaker-diarization","criterion_name":"Speaker Diarization","criterion_definition":"Correctly attributes who said what across a multi-speaker standup.","criterion_evidence_type":"transformation","criterion_rank_role":"decisive","criterion_rank_role_reason":"Correctly attributing who said what is part of making the transcript and notes trustworthy in multi-speaker meetings. (3 of 3 judges)","scenario":"ai-demos-daily-standup-31-july-2026","scenario_name":"AI Demos Daily Standup — 31 July 2026","group_tag":"ai-meeting-notetaker","scenario_description":"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.","modality":"image","input_text":null,"input_artifact_refs":[{"alt":null,"url":"https://cdn.futuresmart.ai/public/aidemos/547dd6f13e4a420fa8ad7bf2c88c7612.png?v=1","role":"input","filename":"31-july-meeting-screenshot.png"}],"stresses":["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"],"verdict":"mixed","score":null,"score_total":null,"note":"Speaker attribution was mostly correct, with nearly all speakers identified by name, but the transcript still showed some misattributed lines, so diarization was not fully reliable for every turn.","evidence_state":"verified","source":null,"artifacts":[{"url":"https://cdn.futuresmart.ai/public/aidemos/f10e8fde29a2451f990006d9cee5ec84.png?v=1","role":"output","alt":null}],"run_id":"ace58582-3d1e-48ee-996c-9b3cd03f27a2","study_title":"AI Meeting Notetakers — Capture Accurate Transcripts, Summaries & Action Items From Live Calls","study_kind":"generation","research_task":"86baxegnv","tested_at":null,"completeness":"input-and-output","input":{"state":"files","text":null,"files":[{"url":"https://cdn.futuresmart.ai/public/aidemos/547dd6f13e4a420fa8ad7bf2c88c7612.png?v=1","filename":"31-july-meeting-screenshot.png","alt":"AI Demos Daily Standup — 31 July 2026","role":"input"}],"modality":"image","stresses":["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"]},"tool_page_slug":"notta","tool_url":"https://aidemos.com/tools/notta","permalink":"https://aidemos.com/evidence/089aaaff-2da4-4739-8045-46df7a2f1e6b","api_url":"https://ai.aidemos.com/v1/observations/089aaaff-2da4-4739-8045-46df7a2f1e6b"},"peers":[{"id":"e9695d25-df6e-448b-b185-4ad01e885bdf","tool":"fathom","tool_name":"Fathom","verdict":"mixed","score":null,"score_total":null,"note":"Fathom 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.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/e6b553ff015d4c309c161b0bb12a1660.png?v=1","evidence_url":"https://aidemos.com/evidence/e9695d25-df6e-448b-b185-4ad01e885bdf"},{"id":"5928b605-59f7-4372-a4b7-914060123f74","tool":"fellow","tool_name":"Fellow","verdict":"worked","score":null,"score_total":null,"note":"The transcript attributed speaker turns correctly across the standup, with all ~10 speakers labeled by name and no attribution errors or generic labels reported.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/f6c095044c7a4498b82f300b683163dd.png?v=1","evidence_url":"https://aidemos.com/evidence/5928b605-59f7-4372-a4b7-914060123f74"},{"id":"a1eae6d9-4eff-41c0-9357-c4b9c8e6f7fb","tool":"fireflies-ai","tool_name":"Fireflies.ai","verdict":"worked","score":null,"score_total":null,"note":"Attributed consecutive turns to distinct speakers in the transcript, and the report says speaker identification was almost complete with only minor attribution errors.","artifact_count":3,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/04c680b706994807aa25f0135512b6ee.png?v=1","evidence_url":"https://aidemos.com/evidence/a1eae6d9-4eff-41c0-9357-c4b9c8e6f7fb"},{"id":"73b827a9-4822-4d1e-a8ad-b6853c5eab4d","tool":"granola","tool_name":"Granola","verdict":"failed","score":null,"score_total":null,"note":"Granola’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.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/ecd4eac38c5f4f1f9e0d671349a4f71a.png?v=1","evidence_url":"https://aidemos.com/evidence/73b827a9-4822-4d1e-a8ad-b6853c5eab4d"},{"id":"472fb01a-44b0-4713-b649-40ee52da5802","tool":"happyscribe","tool_name":"HappyScribe","verdict":"mixed","score":null,"score_total":null,"note":"It 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.","artifact_count":1,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/64855a79c2564122bac93c704797f365.png?v=1","evidence_url":"https://aidemos.com/evidence/472fb01a-44b0-4713-b649-40ee52da5802"},{"id":"4f7e5b21-ff4f-4846-9a07-3219c0659681","tool":"meetgeek","tool_name":"MeetGeek","verdict":"mixed","score":null,"score_total":null,"note":"It 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.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/a909947fdbe64d41b2bc10dca0f44193.png?v=1","evidence_url":"https://aidemos.com/evidence/4f7e5b21-ff4f-4846-9a07-3219c0659681"},{"id":"9552a2b6-0bc3-4802-8f1b-f9cf98c13b46","tool":"otter-ai","tool_name":"Otter.ai","verdict":"failed","score":null,"score_total":null,"note":"Otter’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.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/c2c822851e2f4e71a1ae3b14247c11d3.png?v=1","evidence_url":"https://aidemos.com/evidence/9552a2b6-0bc3-4802-8f1b-f9cf98c13b46"}],"other_criteria":[{"id":"1b0800ea-3b02-4159-88c1-ed437a0b12a7","criterion":"action-item-extraction","criterion_name":"Action-Item Extraction","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"The action-item list extracted the real commitments from the call, formatted them as checkbox items with @mentions, and the report says owner assignment was correct with no false positives.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/1b0800ea-3b02-4159-88c1-ed437a0b12a7"},{"id":"b58378a0-5d37-4751-a711-f2d028fa4401","criterion":"chat-with-notes-ask-questions","criterion_name":"Chat with Notes / Ask Questions","rank_role":"context","verdict":"worked","score":null,"score_total":null,"note":"The 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.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/b58378a0-5d37-4751-a711-f2d028fa4401"},{"id":"d31ec8ee-e1f2-4d37-8cd5-9eeaf5e99255","criterion":"join-method-reliability","criterion_name":"Join Method & Reliability","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"The bot-based Google Meet join was reliable in the tested call: Notta Bot appeared in the meeting list, admitted/managed normally, and the capture ran through the end of the session without disconnects or plan-limit cutoffs.","artifact_count":4,"evidence_url":"https://aidemos.com/evidence/d31ec8ee-e1f2-4d37-8cd5-9eeaf5e99255"},{"id":"3e05ffd9-05fd-44d7-914a-1e1bbb24c13c","criterion":"search-across-notes","criterion_name":"Search Across Notes","rank_role":"context","verdict":"mixed","score":null,"score_total":null,"note":"Search works inside a meeting transcript through AI Chat and returns exact timestamps in plain text, but the timestamps are not clickable, and the report says this was not tested across meetings.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/3e05ffd9-05fd-44d7-914a-1e1bbb24c13c"},{"id":"aa85bfe2-42ee-4df2-b89f-831056fd4fc3","criterion":"summary-quality","criterion_name":"Summary Quality","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"The generated meeting summary was comprehensive and skimmable, with structured sections such as Task & Issue Management and a mindmap-style organization that reflected the meeting flow.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/aa85bfe2-42ee-4df2-b89f-831056fd4fc3"},{"id":"dacc4361-09c8-4791-8895-8a8095e21fe8","criterion":"topic-segmentation","criterion_name":"Topic Segmentation","rank_role":"context","verdict":"worked","score":null,"score_total":null,"note":"The meeting was segmented into useful topic blocks rather than one blob; the report names three sections, including Task & Issue Management, Individual Progress Updates, and API Benchmarking Task.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/dacc4361-09c8-4791-8895-8a8095e21fe8"},{"id":"893f5543-bd03-4e2a-ba30-f6426940628b","criterion":"transcription-accuracy","criterion_name":"Transcription Accuracy","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"Notta’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.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/893f5543-bd03-4e2a-ba30-f6426940628b"}],"appears_in":[{"page_type":"ranking","slug":"ai-meeting-notetakers","title":"Best AI Meeting Notetakers for Accurate Transcripts, Summaries, and Action Items","url":"https://aidemos.com/best/ai-meeting-notetakers","binding":"run"}],"same_scenario":[{"id":"e9695d25-df6e-448b-b185-4ad01e885bdf","tool":"fathom","tool_name":"Fathom","verdict":"mixed","score":null,"score_total":null,"note":"Fathom 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."},{"id":"5928b605-59f7-4372-a4b7-914060123f74","tool":"fellow","tool_name":"Fellow","verdict":"worked","score":null,"score_total":null,"note":"The transcript attributed speaker turns correctly across the standup, with all ~10 speakers labeled by name and no attribution errors or generic labels reported."},{"id":"a1eae6d9-4eff-41c0-9357-c4b9c8e6f7fb","tool":"fireflies-ai","tool_name":"Fireflies.ai","verdict":"worked","score":null,"score_total":null,"note":"Attributed consecutive turns to distinct speakers in the transcript, and the report says speaker identification was almost complete with only minor attribution errors."},{"id":"73b827a9-4822-4d1e-a8ad-b6853c5eab4d","tool":"granola","tool_name":"Granola","verdict":"failed","score":null,"score_total":null,"note":"Granola’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."},{"id":"472fb01a-44b0-4713-b649-40ee52da5802","tool":"happyscribe","tool_name":"HappyScribe","verdict":"mixed","score":null,"score_total":null,"note":"It 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."},{"id":"4f7e5b21-ff4f-4846-9a07-3219c0659681","tool":"meetgeek","tool_name":"MeetGeek","verdict":"mixed","score":null,"score_total":null,"note":"It 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."},{"id":"9552a2b6-0bc3-4802-8f1b-f9cf98c13b46","tool":"otter-ai","tool_name":"Otter.ai","verdict":"failed","score":null,"score_total":null,"note":"Otter’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."}]}