Does AI Project Creator generate serious Next.js AI starters?

Yes. AI Project Creator generates Next.js/OpenAI starter projects with runnable app code, server API routes, environment examples, README setup docs, AGENTS.md handoff notes, guardrails, evals, and deployment guidance. It is designed to be a serious starter rather than a blank visual demo.

What guardrails are included in the generated AI starter?

Generated starters include request validation, server-side API key isolation, Clerk authentication and authorization when enabled, prompt-injection and jailbreak checks, audit logging, safe failure responses, and for AI Agents starters, Zod tool schemas, tool allowlists, and human approval gates for risky tools.

What evals are included in the generated AI starter?

The generated eval pack includes scored checks for answer quality, groundedness, citation readiness, refusal behavior, prompt-injection resistance, and latency. AI Agents starters also include evals for tool selection, tool argument correctness, human approval enforcement, and agent completion rate.

Can the generated AI starter be deployed to Vercel?

Yes. The generated README includes Vercel deployment guidance, a deploy-button pattern, and the environment variables needed for OpenAI, Clerk, Supabase, AI agents, RAG embeddings, or realtime voice depending on the selected project type.

What is an AI project generator?

An AI project generator creates a runnable starter project from selected options. AI Project Generator generates downloadable Next.js/OpenAI starter projects for chatbots, RAG apps, PDF chat, document chat, AI agents, and voice AI agents.

What does AI Project Creator generate?

It generates downloadable Next.js starter projects for OpenAI chatbot UIs, RAG chatbots that answer from uploaded PDFs and documents, AI agents workflows built with the OpenAI Agents SDK, and realtime voice AI agents using the OpenAI Realtime Agents SDK.

Can AI Project Creator generate AI agents?

Yes. The AI Agents project type generates a Next.js/OpenAI starter with @openai/agents, a Prompt Analyzer Agent, a Research Agent, and a server-side /api/agents/chat workflow route.

Can AI Project Creator generate voice AI agents?

Yes. The Voice AI Agents project type generates a Next.js/OpenAI starter with @openai/agents/realtime, a server-side /api/realtime-token route, realtime WebRTC voice session setup, live transcript, mute, interrupt, and typed backchannel controls.

Does AI Project Creator support Supabase for RAG embeddings?

Yes. RAG projects can use local temporary JSON storage or Supabase database storage with pgvector for document chunks and vector embeddings.

Which OpenAI embedding model does the RAG starter use?

The generated RAG project defaults to text-embedding-3-small for document and query embeddings.

Can the generated project run after download?

Yes. Unzip the project, add OPENAI_API_KEY to .env.local, install dependencies, and run the development server.

AI Project Creator

AI project generator for RAG, chatbot, AI agents, and voice AI agents starters.

Download a Next.js + Clerk + Supabase + OpenAI RAG chatbot starter in 30 seconds.

A Next.js starter with auth, guardrails, evals, AGENTS.md handoff, and an architecture Codex or Claude Code can extend.

You are responsible for reviewing, securing, deploying, and operating any generated application. suhasbhairav.com provides starter code only.

NextJSClerkSupabaseOpen AIAI AgentsVoice AI

Project type

RAG storage

Local temporary storageStores vectors in conversions/vector-embeddings/index.json.Supabase databaseStores chunks and embeddings in Supabase Postgres with pgvector.

Project name

Include Clerk AuthAdds ClerkProvider, proxy.ts middleware, signed-in UI gates, and protected API routes to the downloaded starter.

50 presets

Scroll down for more colors

Component colors

PagePanelUser bubbleAssistant bubbleTextAccent

Knowledge Assistant

Talk with PDFs and documents

RAG Chatbot

Upload PDFs or documents here, then ask grounded questions from the generated embeddings.

Can you summarize the customer escalation policy and suggest the next action?

Yes. I would first retrieve the relevant policy chunks, identify the escalation threshold, and draft a concise next action with a human approval checkpoint.

Step 1

Upload docs

Step 2

Embed chunks

Step 3

Retrieve context

Ask about your documents...

Starter quality

Built like a real AI product starter

Production architecture

The download is a complete Next.js app with clean server routes, isolated secrets, environment examples, a polished UI, and setup docs that make it runnable immediately after adding keys.

Safety by default

It includes request validation, Clerk protection when enabled, prompt-injection checks, audit events, and controlled failure messages instead of exposing raw errors.

Serious RAG pipeline

Document upload, parsing, metadata, chunking, embeddings, hybrid retrieval, reranking, citations, source previews, document deletion, and re-indexing are built into the starter.

Product-grade evals

The starter ships with scored eval dimensions for answer quality, groundedness, citations, refusals, injection resistance, approvals, completion, and latency.

One-click deployment

The README includes a Vercel deploy button pattern, required environment variables, and clear deployment notes so the starter can move from local preview to hosted app quickly.

Brandable in minutes

The previewed theme, project type, auth setting, and storage choice are reflected in the generated zip so the starter feels tailored before anyone edits code.