Otter.ai?

KINDAYou can build the valuable core, but the established product has meaningful operational complexity. Pick one narrow promise for v1.

BUILD FIRST

One specific customer type and one primary use case
A vertical slice that completes the main job without manual workarounds
Visible limits, review steps, and a fallback when an external dependency fails

LEAVE FOR LATER

Broad third-party integrations
High-scale collaboration, syncing, and permissions
Automation that can cause harm without human review

RESEARCH NOTES

What this tool actually does.

Checked 2026-09-06

Otter.ai is a cloud-based AI meeting notetaker that records, transcribes, and summarizes meetings in real time. It works across Zoom, Microsoft Teams, and Google Meet via an auto-joining bot (Otter Notetaker) synced to Google/Outlook/iOS calendars, plus a desktop app for bot-free Mac/Windows recording and iOS/Android/Web apps. Features include live transcription with speaker identification, editable speakers/timecodes, synced audio-text playback, live captions for Zoom/Google Meet, automated slide capture, and summaries with decisions, action items, and keywords. Otter AI Chat answers questions across recorded meetings and connected apps. It also imports pre-recorded audio/video files, supports team tagging/comments/action assignment, groups meetings by team/project/topic, and offers admin controls with centralized billing, user management, and reporting. Pricing tiers: Free (25 stored m

SOURCES

RESEARCHED BUILD PROMPT

Give this to your coding agent.

Build "MeetScribe", a focused replacement for Otter.ai's core meeting-notes workflow. The user records a live meeting through a web app or uploads an audio/video file; the system transcribes it with speaker labels and then generates a summary containing decisions, action items, and keywords. Users can edit the transcript and speaker names, and play back the recording with highlighted words synced to audio. Implementation: a single-page React (Vite + TypeScript) frontend and a Node.js/Express backend using SQLite. For live recording, capture mic audio via getUserMedia and MediaRecorder, stream chunks to a /transcribe endpoint (use the Whisper API for segments, or a local whisper.cpp binary), and store word-level timestamps. For uploads, accept MP3/MP4 via multer and run the same pipeline offline. For speaker labels, use a simple heuristic (label change when a new segment has a distinct energy/voice profile) or integrate a diarization model; note this as a limitation. After the meeting, call a /summarize endpoint that uses the OpenAI chat completions API to extract action items, decisions, and keywords from the full transcript. Build the workflow: create a meeting, live/upload recording, editable live transcript panel, edit transcript/speakers, clickable words that scrub playback, summary view with a copyable action-item list, and a meeting list grouped by project/tag with text search. Add auth via bcrypt sessions and store meetings per user. Include a clean, accessible CSS design — do not copy Otter.ai's branding, logo, layout, or design language. Out of scope: calendar sync, Zoom/Teams/Meet bot joining, mobile apps, slide capture, team collaboration, admin dashboards, cross-meeting AI chat, and multi-channel speaker diarization. Provide a README with setup steps, required env vars (OPENAI_API_KEY, port, DB path), architecture overview, and known limitations (diarization accuracy, API latency on long meetings, single-user default, browser support, file size caps).

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