Build your AI Chatbot with AI in under 5 minutes

Build a customer-facing AI chatbot or internal AI assistant — bring your own LLM key, ground it in your own content, and embed it on any site — generated by AI in minutes.

Cómo funciona

Paso 1

Describe tu idea

Escribe un prompt describiendo lo que quieres.

Paso 2

La IA lo construye

FloopFloop genera código listo para producción al instante.

Paso 3

Despliega y publica

Tu proyecto se aloja en su propio subdominio en minutos.

¿Por qué construir con IA en lugar de contratar un desarrollador?

FloopFloopDesarrollador tradicional
Tiempo de lanzamientoMenos de 5 minutos2-8 semanas
CostoDesde $0$5.000 - $50.000+
MantenimientoIncluidoContrato continuo

Prueba estos prompts

Copia cualquier prompt y pégalo en FloopFloop para empezar.

Build a customer-support chatbot for my SaaS that answers questions from a markdown knowledge base. Use OpenAI for the LLM (read OPENAI_API_KEY from project secrets), embed the docs at build time, and surface a sticky chat bubble in the bottom-right corner with a typing indicator and a 'Talk to a human' escape button.

Create an AI assistant for a real-estate site that answers questions about listings. Pull listing data from a JSON file, use Claude as the LLM (ANTHROPIC_API_KEY secret), and render a full-page chat with a sidebar showing the listings the assistant cited in its last answer.

Design an internal AI ops assistant my team can ask about our deployment runbooks. Read .md files from a /runbooks folder, embed them with OpenAI's embeddings, store vectors in-memory, and expose a /chat page with conversation history, source citations, and a simple admin page to upload new runbooks.

Build a multi-tenant AI tutor for a language-learning app. Each user picks a target language, the chatbot adapts its corrections to that language, and conversation history is persisted per user. Show streaming responses, a progress sidebar with a words-learned count, and a daily-streak tracker.

Preguntas frecuentes

Which LLM providers can the chatbot use?
Anything you have an API key for — OpenAI, Anthropic, Google Gemini, Mistral, Groq, OpenRouter, or any OpenAI-compatible endpoint. You add the key as a project secret on the Secrets tab; the generated code reads it from process.env, never from client-side code.
Can the chatbot answer questions about my own content (RAG)?
Yes. Describe what content the chatbot should ground its answers in — a markdown folder, a JSON catalog, a sitemap of pages — and FloopFloop scaffolds the embedding + retrieval pipeline. By default it uses OpenAI embeddings and an in-memory vector store, which is enough for thousands of chunks; for larger corpora ask for Pinecone, Supabase pgvector, or another store.
Will the chatbot stream responses?
Yes — generated chatbots stream tokens by default using server-sent events or the Vercel AI SDK's streaming helpers. The UI ships with a typing indicator and partial rendering so users see the response forming in real time.
Can I embed the chatbot on my existing site?
Two options. (1) Use the chatbot project as-is at its floop.tech subdomain or a custom domain. (2) Ask FloopFloop to ship a JavaScript widget snippet and a public iframe embed; you paste a single <script> tag into your existing site and the chatbot bubble appears in the corner.
How do I keep my LLM costs predictable?
Set a per-conversation token budget in the prompt and FloopFloop wires it into the model call. You can also gate access behind a login (FloopFloop ships built-in auth), set a daily request quota per user, and log every conversation to your database so you can audit cost per customer.
Are conversations private?
By default conversations live on your project's database, or nowhere at all if you ask for ephemeral chats. FloopFloop never reads your users' conversations and the LLM provider's data policy applies — pass {opt_out: true} on OpenAI calls or use Anthropic's no-training endpoints to stop providers from training on your data.

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