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.

작동 방식

단계 1

아이디어를 설명하세요

원하는 것을 일반 텍스트 프롬프트로 작성하세요.

단계 2

AI가 빌드합니다

FloopFloop이 즉시 프로덕션 수준의 코드를 생성합니다.

단계 3

배포 및 라이브 공개

프로젝트가 몇 분 안에 자체 서브도메인에 호스팅됩니다.

개발자 고용 대신 AI로 빌드해야 하는 이유는?

FloopFloop기존 개발자
출시 소요 시간5분 이내2~8주
비용$0부터$5,000 - $50,000+
유지 관리포함지속적인 유지보수 계약

이 프롬프트를 사용해 보세요

아래 프롬프트를 복사하여 FloopFloop에 붙여넣고 시작하세요.

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.

자주 묻는 질문

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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