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

> AI-powered meeting assistant and agentic conversation intelligence platform

- Site: https://sembly.ai
- Category: SaaS / AI Meeting Intelligence
- Verdict: **Serious undertaking** (45/100 vibecodeable)
- Estimated effort: 4-6 weeks of part-time development

## Verdict

Build a personal audio upload and summarization subset, but keep paying for Sembly if you need automated live video call bots.

Vibecoding a personal clone of Sembly's UI, vector search, and document generation engine is entirely feasible over a few weekends. However, replicating the core value—having an automated bot reliably dial into live Zoom, Teams, and Google Meet calls, bypass lobby permissions, capture multi-stream audio, and handle enterprise-grade speaker diarization—involves brittle headless browser engineering and complex WebRTC handling that will consume weeks of frustrating debugging.

### What you can't replicate

- Enterprise security compliance seals (SOC 2 Type II, HIPAA audit)
- Native marketplace installations for Zoom and Microsoft Teams
- Proprietary bot dialing infrastructure that never gets blocked by host security settings

## What it does

Joins live video calls across Zoom, Teams, and Google Meet to transcribe, diarize, and convert conversations into client-ready deliverables, risk logs, and CRM records.

### Core features

- Automatic virtual meeting bot that joins Zoom, Google Meet, and Microsoft Teams
- Audio transcription with speaker diarization across multiple languages
- Multi-meeting RAG chat interface for querying historical transcripts
- Automated extraction of action items, risks, and notable events
- Client-ready deliverable generation (proposals, investment memos, project briefs)
- CRM and task tracker synchronization webhooks

## The business

### Pricing

- Basic: $17/mo — For professionals who want every meeting captured and structured.
- Pro: $29/user/mo — For teams that rely on meeting intelligence to track decisions.
- MAX: $39/user/mo — For organizations requiring advanced intelligence and audit logs.

### Funding

$8M+ raised.
- Seed
Investors: MI-GSO | PCUBED

Founded 2019.
Team size: 24-27.

## The hard parts

- Building a headless browser or WebRTC bot framework that reliably auto-joins secure enterprise video calls without crashing or getting blocked
- Implementing precise multi-speaker diarization and cross-talk handling over long audio recordings
- Orchestrating vector search and prompt-chaining pipelines to synthesize multi-document meeting history into structured executive deliverables
- Handling real-time token streaming and asynchronous background processing for large media uploads up to 5 hours long

## How to vibe code Sembly AI

### Prerequisites

- Node.js (free): Runtime environment for the full-stack web application.
- GitHub (free): Version control and CI/CD pipeline triggers.
- AssemblyAI Account (Pay-as-you-go (~$0.12/hr)): Provides automatic speech recognition and speaker diarization APIs.

### Recommended AI tools

- Claude Code: Best-in-class multi-file agentic coding tool for scaffolding full-stack architectures and complex backend pipelines.
- Cursor: Ideal for inspecting real-time code diffs and refining React component styling.

### Stack

- Frontend: Next.js
- Backend: Next.js Server Actions / Node.js
- Database: Neon
- Auth: better-auth
- Payments: none
- Other: AssemblyAI, OpenAI API, Vercel AI SDK, Resend

### Hosting

- Vercel (Hosting the Next.js frontend, API routes, and serverless background actions.): $0-20/mo
- Neon (Serverless Postgres database with pgvector support for meeting transcript embeddings.): $0/mo

### Build guide

1. **Project Scaffolding & Database Schema** — Initialize the Next.js application with TypeScript, Tailwind CSS, and configure the Neon Postgres database with pgvector extensions.

```
Scaffold a new Next.js project using Tailwind CSS and TypeScript. Set up better-auth for secure user authentication with email and password. Configure Drizzle ORM to connect to a Neon Postgres database. Define database schemas for users, meetings (id, title, transcript_text, audio_url, duration, created_at), transcripts (id, meeting_id, speaker_name, start_time, end_time, text), and deliverables (id, meeting_id, type, content). Ensure pgvector extension is enabled for semantic search embeddings. Include error handling and strict TypeScript types throughout.
```

2. **Audio Upload & Transcription Pipeline** — Build an audio file upload interface and integrate the AssemblyAI API for speech-to-text transcription and speaker diarization.

```
Build a React page and API route in Next.js that accepts audio file uploads (MP3, WAV, M4A up to 500MB). Save the file temporarily or stream it to AssemblyAI using their Node.js SDK with speaker diarization enabled. Once the webhook or polling completes, parse the JSON response containing utterance segments with speaker labels, timestamps, and confidence scores. Store these structured utterances into the Neon database linked to the user's meeting record. Handle file size validation and display upload progress in the UI.
```

3. **AI Meeting Summarization & Deliverable Generator** — Implement automated AI processing using the Vercel AI SDK to generate executive summaries, action items, risks, and client-ready documents from transcripts.

```
Create a server action that triggers after a meeting transcript is successfully saved. Using the Vercel AI SDK and Anthropic Claude via API, process the full transcript text to extract: 1) Executive summary, 2) Key action items with assignees and deadlines, 3) Risks and issues discussed, 4) A client-ready deliverable (e.g., proposal draft or project brief). Store these generated artifacts in the deliverables table. Build a dynamic dashboard view in Next.js to render meeting notes, timestamps, and export options.
```

4. **Multi-Meeting RAG Chat Interface** — Develop an interactive chat assistant capable of querying across multiple historical meeting transcripts using vector embeddings.

```
Build a multi-meeting AI chat interface in Next.js using the useChat hook from the Vercel AI SDK. When a user asks a question, generate an embedding of the query using OpenAI's embedding model, perform a cosine similarity search against stored transcript chunks in Neon Postgres via pgvector, and inject the most relevant meeting context into the LLM system prompt. Return streamed responses with citations pointing to specific meetings and timestamps.
```

5. **Analytics Dashboard & Polish** — Construct workspace analytics, search filters, and UI polish to simulate a production-grade meeting intelligence platform.

```
Build a workspace analytics dashboard aggregating user meeting stats: total meetings recorded, total hours spent in calls, breakdown of action items by status, and recent sentiment trends. Implement advanced search filters allowing users to filter transcripts by keyword, speaker, or date range. Polish the UI with clean Tailwind styling, loading skeletons, and responsive layouts suitable for professional workflows.
```

### Cost vs paying

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

- Domain name: $12/yr
- AssemblyAI initial credits: $10
- Total: ~$22 one-time

**Ongoing costs (monthly):**

- Vercel Hobby/Pro Hosting: $0-20/mo
- AssemblyAI & OpenAI API usage: ~$5-15/mo
- Total: ~$5-35/mo

- Paying for the SaaS instead: $29/user/mo (Pro Plan)
- Build time: 35-50 hours
- AI tool credits: $20 (Claude Pro / Cursor)
- Break-even: 1 month of Pro plan subscription

## Sources

- [Sembly AI Official Website](https://sembly.ai)
- [Sembly Pricing Page](https://sembly.ai/pricing)
- [StartEngine Campaign for Sembly AI](https://www.startengine.com/offer/sembly)