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

> AI-powered speech-to-text transcription and meeting assistant

- Site: https://transkriptor.com
- Category: Productivity & AI
- Verdict: **Solid side project** (68/100 vibecodeable)
- Estimated effort: 2-3 weeks part-time

## Verdict

Build a personal transcription dashboard with Next.js, OpenAI Whisper, and an LLM chat layer in a couple of weeks, but skip the automated meeting bots.

The core loop of uploading an audio file, transcribing it via Whisper, storing chunks in a Postgres database with pgvector, and chatting with an LLM about the transcript is a classic side project. What you drop for personal use are the live Zoom/Teams recording bots, which require managing headless browser instances and fragile WebRTC audio streams that break whenever video platforms update their UI.

### What you can't replicate

- Reliable headless browser bots that bypass bot detection on corporate Zoom, Microsoft Teams, and Google Meet calls
- Enterprise SOC 2 and GDPR compliance infrastructure
- Organic brand trust and millions of processed minutes of feedback data

## What it does

Convert audio and video recordings or live meetings into accurate, searchable text across 100+ languages with automatic speaker diarization and an LLM-driven query assistant.

### Core features

- Audio/video file upload handler (MP3, MP4, WAV)
- Speech-to-text transcription pipeline with speaker diarization
- Interactive transcript editor with timestamped playback
- AI meeting summaries and automated action item extraction
- Ask Transkriptor chat layer with RAG over transcript history
- Export transcripts to TXT, SRT, VTT, and Word
- Multi-language translation support

## The business

### Pricing

- Lite: $9.99/mo
- Pro: $19.99/mo
- Team: $30/mo/seat

Founded 2021.
Team size: Unknown.

## The hard parts

- Building and maintaining real-time automated meeting bots for Zoom, Teams, and Google Meet without getting blocked
- Handling large audio files, chunking, and aligning precise timestamped speaker diarization
- Building a performant RAG vector pipeline across hundreds of long transcripts with citation mapping

## How to vibe code Transkriptor

### Prerequisites

- Node.js (free): Runtime for running the full-stack Next.js application.
- GitHub (free): Source code repository and CI/CD deployment connection.
- OpenAI API Key (Pay-as-you-go (~$0.006/min)): Required for Whisper transcription and GPT model embeddings/chat.

### Recommended AI tools

- Claude Code: Best-in-class coding agent for scaffolding multi-file Next.js apps, setting up database schemas, and wiring API endpoints.
- Cursor: Ideal AI editor for refining UI components, transcript editors, and audio playback timelines.

### Stack

- Frontend: Next.js with Tailwind CSS and shadcn/ui
- Backend: Next.js API Routes / Server Actions
- Database: Neon (Serverless Postgres with pgvector)
- Auth: better-auth
- Payments: None (Personal use clone)
- Other: Vercel AI SDK, OpenAI API (Whisper + GPT), UploadThing

### Hosting

- Vercel (Hosting the Next.js frontend, API routes, and edge serverless functions.): $0-20/mo
- Neon (Storing user accounts, transcripts, and vector embeddings for semantic retrieval.): $0/mo

### Build guide

1. **Project Scaffolding and Database Setup** — Initialize a Next.js project with Tailwind CSS, configure better-auth for single-user authentication, and set up Neon Postgres with the pgvector extension enabled.

```
Create a new Next.js project using App Router, TypeScript, and Tailwind CSS. Install shadcn/ui primitives (button, dialog, dropdown-menu, tabs, card). Configure better-auth with email/password authentication backed by a Neon Postgres database connection using Prisma or Drizzle. Ensure the database schema includes tables for `Transcripts` (id, title, audio_url, raw_text, duration, created_at) and `TranscriptSegments` (id, transcript_id, speaker_label, start_time, end_time, text, embedding vector(1536)). Write a migration script to enable the pgvector extension.
```

2. **Audio Upload and Transcription Pipeline** — Implement file upload handling for MP3, MP4, and WAV files, and integrate the OpenAI Whisper API to generate timestamped transcription segments.

```
Build an audio file upload component supporting MP3, MP4, and WAV formats up to 100MB using UploadThing. When an upload completes, trigger a server action that sends the audio file to the OpenAI Whisper API (`v1/audio/transcriptions`) requesting verbose_json output with timestamps. Parse the response into structured segments with speaker labels, start times, and end times. Save the full transcript and individual segments to the database.
```

3. **Interactive Transcript Editor and Media Player** — Create a split-pane dashboard view with an HTML5 audio player on one side and a synchronized, editable transcript view on the other.

```
Build a dashboard page for viewing a single transcript (`/dashboard/transcripts/[id]`). Display an HTML5 audio player at the top with play/pause and scrub controls. Below the player, render the list of transcript segments with timestamps and speaker tags. Clicking any timestamp should seek the audio player to that exact second. Make the transcript text inline-editable so users can correct transcription errors, saving changes back to the database on blur.
```

4. **AI Summaries and Action Item Extraction** — Add backend processing to generate executive summaries and extract actionable tasks from transcripts using Anthropic or OpenAI models via the Vercel AI SDK.

```
Implement an AI summary feature using the Vercel AI SDK and Anthropic Claude. Add a 'Generate Summary' button on the transcript view that sends the raw transcript text to an API route, prompting the LLM to return a structured JSON response containing: an executive summary, key topics, and a list of action items with owners and deadlines. Display these nicely in a dedicated tab on the transcript page and persist them in the database.
```

5. **Ask Transkriptor Chat and RAG Knowledge Base** — Build a conversational chat interface that queries past transcripts using vector embeddings and RAG to answer user questions with citations.

```
Build an 'Ask Transkriptor' chat interface using the Vercel AI SDK `useChat` hook. When a user asks a question, generate an embedding of the query using OpenAI's embedding model, perform a vector similarity search against the `TranscriptSegments` table using pgvector in Neon, and retrieve the top relevant chunks. Inject these chunks into the LLM system prompt as context with meeting titles and timestamps. Ensure the AI response cites the exact meeting name and timestamp for every claim made.
```

6. **Export Formats and Polish** — Implement export functions to download transcripts as TXT, SRT subtitle files, and Word documents, and apply final UI polish.

```
Add an export dropdown menu to the transcript view allowing users to download the transcript in three formats: Plain Text (.txt), Microsoft Word (.docx), and Subtitle Format (.srt with proper time formatting like 00:01:20,123 --> 00:01:25,456). Polish the overall dashboard UI with responsive navigation, loading skeletons for transcription jobs, and error handling for failed API calls.
```

### Cost vs paying

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

- AI coding assistant subscription: $20.00
- OpenAI API starting credits: $10.00
- Total: ~$30.00 one-time

**Ongoing costs (monthly):**

- Vercel Hobby hosting: $0.00
- Neon Serverless Postgres: $0.00
- OpenAI Whisper & LLM API usage (approx 300 mins/mo): ~$3.00/mo
- Total: ~$3.00/mo

- Paying for the SaaS instead: $19.99/mo (Pro Plan)
- Build time: 25-35 hours
- AI tool credits: $20 (Claude Pro or Cursor Pro)
- Break-even: 2 months

## Sources

- [Transkriptor Home Page](https://transkriptor.com)
- [Transkriptor Pricing Page](https://transkriptor.com/pricing)
- [Transkriptor Security Page](https://transkriptor.com/security)