{"observation":{"id":"5a191ed7-02a9-4979-931d-9219e0b75fce","tool":"meetgeek","tool_name":"MeetGeek","criterion":"transcription-accuracy","criterion_name":"Transcription Accuracy","criterion_definition":"Word accuracy on the shared call, especially names, tools, numbers, and jargon.","criterion_evidence_type":"transformation","criterion_rank_role":"decisive","criterion_rank_role_reason":"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)","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":"worked","score":null,"score_total":null,"note":"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\".","evidence_state":"verified","source":null,"artifacts":[{"url":"https://cdn.futuresmart.ai/public/aidemos/f3fbc51e628c42c19595b82c5e481a25.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":"meetgeek","tool_url":"https://aidemos.com/tools/meetgeek","permalink":"https://aidemos.com/evidence/5a191ed7-02a9-4979-931d-9219e0b75fce","api_url":"https://ai.aidemos.com/v1/observations/5a191ed7-02a9-4979-931d-9219e0b75fce"},"peers":[{"id":"1c4f4fe3-3ed8-490d-992b-a25ddd604669","tool":"fathom","tool_name":"Fathom","verdict":"mixed","score":null,"score_total":null,"note":"Fathom'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.\"","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/19b062d60f8e45a6a5fa364948287362.png?v=1","evidence_url":"https://aidemos.com/evidence/1c4f4fe3-3ed8-490d-992b-a25ddd604669"},{"id":"d4b52ad2-b536-4580-99fa-644f4b772a93","tool":"fellow","tool_name":"Fellow","verdict":"worked","score":null,"score_total":null,"note":"The 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.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/7476ae6a377e411094940f94f34b21f9.png?v=1","evidence_url":"https://aidemos.com/evidence/d4b52ad2-b536-4580-99fa-644f4b772a93"},{"id":"bac222c7-f7cf-4041-8449-df5aa22c88ac","tool":"fireflies-ai","tool_name":"Fireflies.ai","verdict":"worked","score":null,"score_total":null,"note":"Generated 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.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/7aecac3bebb2479aa0f18cf6c9f1f0db.png?v=1","evidence_url":"https://aidemos.com/evidence/bac222c7-f7cf-4041-8449-df5aa22c88ac"},{"id":"87d23b55-bc59-4d3e-8547-9b80fc1107f6","tool":"granola","tool_name":"Granola","verdict":"failed","score":null,"score_total":null,"note":"On 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.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/d3ec039f484d40328057665e51ba34de.png?v=1","evidence_url":"https://aidemos.com/evidence/87d23b55-bc59-4d3e-8547-9b80fc1107f6"},{"id":"624ffd2e-c9da-4fb4-acc3-041a5b115a50","tool":"happyscribe","tool_name":"HappyScribe","verdict":"mixed","score":null,"score_total":null,"note":"On 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.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/768c8f926786411984134803cb5396a5.png?v=1","evidence_url":"https://aidemos.com/evidence/624ffd2e-c9da-4fb4-acc3-041a5b115a50"},{"id":"893f5543-bd03-4e2a-ba30-f6426940628b","tool":"notta","tool_name":"Notta","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,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/86173a6a33c7494b96de40392d0740b6.png?v=1","evidence_url":"https://aidemos.com/evidence/893f5543-bd03-4e2a-ba30-f6426940628b"},{"id":"0efa5738-c840-44ba-a72a-b35d289e70fb","tool":"otter-ai","tool_name":"Otter.ai","verdict":"worked","score":null,"score_total":null,"note":"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.","artifact_count":3,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/0db942cf24dd4c3f9383ac4f5208e840.png?v=1","evidence_url":"https://aidemos.com/evidence/0efa5738-c840-44ba-a72a-b35d289e70fb"}],"other_criteria":[{"id":"dfeacb85-baa0-4c8b-a44e-e4a7b005d54f","criterion":"action-item-extraction","criterion_name":"Action-Item Extraction","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"It 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.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/dfeacb85-baa0-4c8b-a44e-e4a7b005d54f"},{"id":"75aaf0b0-c9d5-4376-8b81-5b6f2574c0fb","criterion":"chat-with-notes-ask-questions","criterion_name":"Chat with Notes / Ask Questions","rank_role":"context","verdict":"mixed","score":null,"score_total":null,"note":"It 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.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/75aaf0b0-c9d5-4376-8b81-5b6f2574c0fb"},{"id":"0a57537c-4ad4-4b2f-8115-4281f671bd30","criterion":"editability","criterion_name":"Editability","rank_role":"context","verdict":"worked","score":null,"score_total":null,"note":"Users 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.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/0a57537c-4ad4-4b2f-8115-4281f671bd30"},{"id":"9ebe6fbe-6f09-440e-afde-66593a710483","criterion":"join-method-reliability","criterion_name":"Join Method & Reliability","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"The bot successfully joined a Google Meet call and the report says it captured the full ~30-minute meeting with zero disconnections or data loss.","artifact_count":3,"evidence_url":"https://aidemos.com/evidence/9ebe6fbe-6f09-440e-afde-66593a710483"},{"id":"e8929c90-b25d-47cf-ab27-f340ed881ef4","criterion":"search-across-notes","criterion_name":"Search Across Notes","rank_role":"context","verdict":"worked","score":null,"score_total":null,"note":"It 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.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/e8929c90-b25d-47cf-ab27-f340ed881ef4"},{"id":"4f7e5b21-ff4f-4846-9a07-3219c0659681","criterion":"speaker-diarization","criterion_name":"Speaker Diarization","rank_role":"decisive","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,"evidence_url":"https://aidemos.com/evidence/4f7e5b21-ff4f-4846-9a07-3219c0659681"},{"id":"b482399a-b91b-4aaa-b6f1-a56363792548","criterion":"summary-quality","criterion_name":"Summary Quality","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"It 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.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/b482399a-b91b-4aaa-b6f1-a56363792548"},{"id":"b45a857b-9306-4a29-a568-dcdf9f79fdf2","criterion":"topic-segmentation","criterion_name":"Topic Segmentation","rank_role":"context","verdict":"worked","score":null,"score_total":null,"note":"It 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.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/b45a857b-9306-4a29-a568-dcdf9f79fdf2"}],"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":"1c4f4fe3-3ed8-490d-992b-a25ddd604669","tool":"fathom","tool_name":"Fathom","verdict":"mixed","score":null,"score_total":null,"note":"Fathom'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.\""},{"id":"d4b52ad2-b536-4580-99fa-644f4b772a93","tool":"fellow","tool_name":"Fellow","verdict":"worked","score":null,"score_total":null,"note":"The 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."},{"id":"bac222c7-f7cf-4041-8449-df5aa22c88ac","tool":"fireflies-ai","tool_name":"Fireflies.ai","verdict":"worked","score":null,"score_total":null,"note":"Generated 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."},{"id":"87d23b55-bc59-4d3e-8547-9b80fc1107f6","tool":"granola","tool_name":"Granola","verdict":"failed","score":null,"score_total":null,"note":"On 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."},{"id":"624ffd2e-c9da-4fb4-acc3-041a5b115a50","tool":"happyscribe","tool_name":"HappyScribe","verdict":"mixed","score":null,"score_total":null,"note":"On 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."},{"id":"893f5543-bd03-4e2a-ba30-f6426940628b","tool":"notta","tool_name":"Notta","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."},{"id":"0efa5738-c840-44ba-a72a-b35d289e70fb","tool":"otter-ai","tool_name":"Otter.ai","verdict":"worked","score":null,"score_total":null,"note":"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."}]}