# How to Vibe Code Your Own Granola (and Stop Paying for It)

> The AI notepad for people in back-to-back meetings

- Site: https://granola.ai
- Category: Productivity & AI Note-taking
- Platforms: macOS app, Windows app
- Verdict: **Serious undertaking** (45/100 vibecodeable)
- Estimated effort: 4-6 weeks of focused building and debugging

## Verdict

Build a personal subset script with Python and local Whisper, but keep paying for Granola if you rely on the polished native desktop app.

Replicating Granola requires writing native audio-capture code for macOS and Windows, integrating real-time transcription streams, and setting up a vector database for cross-meeting RAG. While an AI coding agent can scaffold the frontend dashboard and LLM prompts, wrestling with OS-level audio device permissions, loopback drivers, and background daemon reliability is a multi-week engineering slog.

### What you can't replicate

- The massive brand network effect and viral prestige among tech elite
- Seamless zero-friction enterprise procurement and security posture (SOC 2, SCIM)
- The exact polish of a native desktop menubar utility with rock-solid background audio hooks

## What it does

An AI-powered meeting notepad and context platform that operates locally on desktop, recording and transcribing system audio without injecting meeting bots.

### Core features

- Native desktop audio capture (system audio + mic without meeting bots)
- Real-time transcription via speech-to-text APIs
- Calendar integration for meeting detection and automated pre-meeting briefs
- Interactive AI notepad combining raw user notes with live transcript streams
- Post-meeting action item extraction and follow-up email generation
- Cross-meeting conversational retrieval (RAG / AI chat across history)
- Shared team Spaces and markdown export workflows

## The business

### Pricing

- Basic: Free
- Business: $14/mo
- Enterprise: $35/mo

### Funding

$192M raised.
- Seed (April 2023)
- Series A: $20M (October 2024)
- Series B: $43M (May 2025)
- Series C: $125M at $1.5B valuation (March 2026)
Investors: Spark Capital, Index Ventures, Kleiner Perkins, Daniel Gross & Nat Friedman (NFDG), Lightspeed, Accel

Founded 2023.
Team size: 20-50.

## The hard parts

- Writing low-level macOS/Windows audio interception hooks for system-wide loopback recording
- Synchronizing asynchronous streaming transcripts with sparse user-written bullet points instantaneously
- Vector-indexing hundreds of historical transcripts for sub-second cross-meeting contextual search
- Managing native desktop app state, window management, and background recording stability

## How to vibe code Granola

### Prerequisites

- macOS + Xcode (free): Required for compiling native desktop audio capture bindings and UI menus
- Node.js (free): Runtime for Tauri frontend build system and backend services
- Anthropic API Key (pay-as-you-go): Powers the AI notepad enhancement and cross-meeting chat reasoning

### Recommended AI tools

- Claude Code: Best-in-class agentic coding tool for scaffolding multi-file native/web hybrids and wiring backend services
- Cursor: Ideal for iterative UI work on the notes dashboard and markdown editor components

### Stack

- Frontend: Next.js + Tailwind CSS wrapped in Tauri for native desktop distribution
- Backend: Node.js with Express or Next.js API routes
- Database: Turso (SQLite for local-first storage of transcripts and notes)
- Auth: better-auth for simple local session management
- Payments: none (personal use clone)
- Other: OpenAI Whisper / Deepgram API for transcription, Anthropic API for note enhancement, Tailwind CSS for styling

### Hosting

- Cloudflare (Hosting backend API and vector metadata storage): $0/mo

### Build guide

1. **Scaffold Tauri Desktop Shell & Audio Hook Foundation** — Initialize a Tauri v2 project combining a web frontend with a Rust backend. Implement OS-level microphone and system audio capture hooks to record raw PCM audio buffers into temporary WAV files.

```
Create a new Tauri v2 project configured with a Next.js frontend and a Rust backend. Write Rust native commands using CoreAudio (macOS) or WASAPI (Windows) to capture combined system audio and microphone input into a local WAV file when triggered. Expose Tauri IPC commands to start and stop recording, returning the file path upon completion. Ensure robust error handling for OS permission prompts regarding microphone and screen/audio recording access. Verify compilation and test the audio recording loop locally.
```

2. **Integrate Transcription Pipeline** — Connect the recorded audio output to OpenAI Whisper API or Deepgram for fast, accurate speech-to-text conversion upon meeting completion.

```
Build a Node.js transcription service module that takes a local audio file path and submits it to the OpenAI Whisper API or Deepgram API with timestamp options enabled. Handle multipart form uploads securely and return a structured JSON transcript containing timestamped utterances and speaker labels. Add retry logic for network drops and output the parsed transcript to the local SQLite database.
```

3. **Calendar Sync & Brief Generator** — Integrate Google Calendar and Microsoft Outlook APIs to automatically detect upcoming meetings and fetch attendee history.

```
Implement OAuth authentication routes for Google Calendar and Microsoft Graph. Create a background sync service that polls upcoming calendar events for the authenticated user, extracts attendee metadata, and queries past meeting notes in Turso to construct a pre-meeting brief prompt. Display these briefs in a dedicated dashboard view prior to meeting start times.
```

4. **AI Notepad & Transcript Merging Engine** — Build the split-screen meeting notepad where users type manual bullet points while audio records in the background, merging instantly with the transcript via Claude upon meeting end.

```
Build a React-based meeting workspace interface in Next.js featuring a real-time markdown notepad on one side and a live or post-meeting transcript viewer on the other. When the user clicks 'End Meeting', send the raw user bullet points and the full Whisper transcript to an API route powered by the Anthropic Claude API. Instruct Claude to merge sparse notes with transcript context to generate polished meeting notes, action items, and a follow-up email. Render the output cleanly in the UI with one-click copy and export actions.
```

5. **Cross-Meeting RAG & AI Chat (Granola Chat)** — Implement vector embeddings for all stored meeting notes and transcripts, enabling semantic search and conversational chat across all historical meetings.

```
Set up vector embeddings generation using OpenAI or Anthropic embedding endpoints for all saved meeting transcripts and enhanced notes, storing vectors in Turso or a local SQLite vector extension. Build a 'Granola Chat' sidebar interface where users can type natural language queries across their entire meeting history. Implement semantic search retrieval-augmented generation (RAG) to inject relevant past meeting excerpts into the LLM context window and stream conversational answers back to the user.
```

### Cost vs paying

**Starting costs (one-time):**

- AI API Credits (Anthropic / OpenAI): $15 one-time
- Total: ~$15 one-time

**Ongoing costs (monthly):**

- Transcription & LLM API Usage: ~$5-15/mo
- Total: ~$10/mo

- Paying for the SaaS instead: $14/mo (Business Plan)
- Build time: 35-50 hours
- AI tool credits: $20 (Claude Pro)
- Break-even: 1 month of API usage vs subscription if heavy user

## Sources

- [Granola Official Website](https://granola.ai)
- [Sifted: Granola raises $125M Series C at $1.5bn valuation](https://sifted.eu/articles/granola-ai-unicorn-series-c-125m)