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AnotherWrapper Premium is a production-oriented Next.js starter for AI products. These docs are meant to help both non-technical buyers and developers understand what is included, how it works, and where to edit it.

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What This Repo Includes

The repo combines three things in one place:
  • a public marketing site
  • a signed-in product with multiple AI apps
  • the infrastructure needed to sell and run that product
That means you are not starting from a blank app shell. You get the customer experience, the internal product experience, and the core SaaS plumbing in one codebase.

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What The Docs Cover

These docs are split by job:
  • Start Here for onboarding, configuration, troubleshooting, and launch
  • Setup for getting the app running
  • Codebase for understanding the repo layout
  • Platform for services like Supabase, storage, AI SDK, analytics, email, and SEO
  • Backend for database and API architecture
  • Providers for OpenAI, Anthropic, Google, xAI, Groq, DeepSeek, Replicate, and ElevenLabs
  • Apps for the actual demo products included in the premium package
  • Recipes for common customization tasks

Quick Setup

Get the repo running locally with the minimum required setup.

Configuration

Understand the env vars and config surface before you start editing.

Apps Overview

See what each included app does and which providers it needs.

AI SDK

Learn how streaming, tool calling, and structured generation are wired.

Vector RAG

Learn how document chat, embeddings, and citations work.

Launch Checklist

Review the production checklist before shipping.

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How To Read These Docs

If you are new to the repo, this order works well:
  1. Introduction
  2. Configuration
  3. Project Structure
  4. Apps Overview
  5. the specific provider and service pages you plan to use
If you are already building on top of it, jump straight to:

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Internal Vs Public Docs

This docs site is the public, customer-safe layer of the repo.
  • public docs live in docs/public
  • internal maintainers notes live in docs/architecture
That separation is intentional. It keeps the docs useful for customers without publishing internal planning or implementation notes that do not belong in the public handbook.