# Build brief — a focused alternative to Otter.ai

> **Verdict:** Partly, if you narrow it · **Buildability:** 53/100 · **Category:** Meeting Notes
> **Source:** https://www.canitbevibecoded.com/otter-ai
> Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from Otter.ai. Verify current pricing and capabilities before acting.

## Context

**Otter.ai** — Meeting transcription, summaries, and AI chat over conversations. It currently costs $16.99/mo.

You can build transcription and summaries, but Otter's value includes live meeting assistant behavior, account sync, speaker workflow, integrations, and mobile/web reliability.

This brief describes a focused, single-operator replacement for the part of Otter.ai that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.

## What you are building

Use a meeting bot or local recorder, run transcription, diarize speakers, summarize, then expose search/chat over transcripts.

- Capture supplied audio, transcribe it, create structured notes, and export them.
- A responsive interface with real empty, loading, success, and error states.

## Requirements

### Functional

- Storage/search index.
- Calendar/video-call integration if bot-style capture is desired.

### Data and integrations

- Speech-to-text API or local Whisper.

Each of these needs a real account, credential, or quota. Set them up before writing feature code.

### Non-functional

- Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
- Security: server-side secrets, validated input, and no credentials in the client bundle.
- Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
- Portability: the operator can export their data and leave without losing it.

## Implementation brief

Build me a personal meeting transcription and search tool to replace Otter.ai.
Requirements:

- Python stack: whisperX (faster-whisper backend) for transcription, Flask for the UI,
  stdlib sqlite3 for storage.
- A CLI: `otter record` captures the mic to ~/Meetings/YYYY-MM-DD-HHMM/audio.wav; `otter
  import file.m4a` handles recordings made elsewhere.
- Transcribe locally with whisperX, word timestamps plus speaker diarization; label
  speakers SPEAKER_1/2 and let me rename them once per meeting.
- Send the transcript to an LLM (key in .env) for a summary: 5 bullets, decisions made,
  action items with owners. Save transcript.md and summary.md next to the audio.
- Index transcripts into SQLite FTS5; `otter search "budget"` returns matching lines
  with meeting date and timestamp.
- A minimal page on localhost:8787: meeting list, one search box, and an ask box that
  answers questions over a chosen transcript via the LLM.
- Everything stays on my machine except the LLM calls; no accounts, no telemetry.
- Out of scope: a bot that joins Zoom/Meet calls, mobile apps, and team sharing.
  Diarization will be rough on crosstalk, accept it.
- README: Python and ffmpeg install, model download size, and the macOS mic permission.

## Delivery standard

- Inspect the repository first, then write a short implementation plan before writing code.
- Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
- Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
- Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
- Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
- Add structured logs around every external call and return actionable errors without leaking sensitive details.
- Write unit tests for the core logic and one automated test of the main user journey.
- Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.

## Acceptance criteria

- [ ] A clean install starts the app using only the README and .env.example.
- [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
- [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
- [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
- [ ] Tests, type checking, linting, and a production build all pass with no ignored failures.
- [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.

## Non-goals

Do not build these, and do not claim to have replaced them:

- Live bot joining meetings.
- Speaker diarization quality.
- Mobile apps.
- Team/admin controls.
- Connectors, OAuth flows, and vendor API changes require constant upkeep.
- Permissions, presence, and shared workflows are difficult to simplify.

## What you still own after launch

- Secure credentials, rotate secrets, and handle provider rate limits.
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.
- Maintain every third-party integration as APIs and OAuth rules change.

## Risk

**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.

Editorial confidence in this assessment: medium. No independent one-shot implementation is linked yet.

## Prior art

Working open-source software you can read, fork, or borrow from before starting:

- [whisperX](https://github.com/m-bain/whisperX) — Open-source transcription alignment and diarization tooling useful for DIY Otter-like work

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Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/otter-ai