I'm trying to prototype a private Q&A app for our company docs. What's the fastest stack to start with?

If your goal is fastest possible prototype for a private internal Q&A app over company docs, I’d start with:

Fastest practical stack

• Frontend: Next.js
• Auth: Clerk or Auth.js
• LLM app framework: LlamaIndex or LangChain
• Vector store: Postgres + pgvector or Supabase
• Embeddings: OpenAI or Voyage
• LLM: OpenAI / Anthropic
• Doc ingestion: PDF/Docs → text extraction with Unstructured, pdfplumber, or built-in parsers
• Deployment: Vercel for the app, Supabase/Neon for DB

Why this is the fastest

• Next.js gives you UI + API routes in one codebase
• Supabase/Postgres avoids setting up a separate vector DB
• LlamaIndex is usually the quickest for “chat with docs” prototypes
• Auth can be added quickly for private access
• Vercel makes deployment easy

Simplest architecture

1. Upload documents
2. Extract text and chunk it
3. Create embeddings
4. Store chunks + embeddings in Postgres/pgvector
5. On question:
   • embed the query
   • retrieve top relevant chunks
   • send them to the LLM with the question
6. Return answer with citations/snippets

If you want absolute minimum setup

Use:

• Next.js
• Supabase
• OpenAI API
• LlamaIndex
• Vercel

That’s probably the quickest path from zero to working demo.

If you care about internal privacy

A few practical considerations:

• Keep docs in your own DB/bucket
• Use a model provider with clear data controls
• Add row-level security if using Supabase
• Log queries carefully, since internal questions may contain sensitive info
• Add basic access control per user/team/document

My recommendation

For a first prototype:

• Next.js + Supabase + LlamaIndex + OpenAI + Vercel

If you want, I can also give you:

1. a minimal folder structure,
2. a step-by-step build plan, or
3. the fastest stack if you want to self-host everything.