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

What is a ai chatbot?

An AI chatbot is a conversational interface backed by a large language model — OpenAI, Anthropic, Gemini, or a local model — that handles a specific job: customer support, sales qualification, internal knowledge lookup, code assistance, or interactive learning. The shape of the product is well-understood now (chat thread, message input, streaming responses, a system prompt that gives the bot personality and limits) but the engineering around it is the part that takes weeks: key management, rate limiting, retrieval-augmented grounding so the bot answers from your data rather than hallucinating, embedding it on your existing site, and the analytics to know which conversations are working. The sweet spot for most teams is a small focused bot with one job — a docs-search assistant, a 'first-line' support triage bot, a recipe coach — rather than a general-purpose chat that competes with ChatGPT.

Common features

  • Streaming responses with token-by-token rendering
  • System prompt that sets persona, scope, and guard-rails
  • Bring-your-own-key — OpenAI, Anthropic, Gemini, or others
  • Retrieval-augmented grounding on your own documents
  • Conversation history per user with retention controls
  • Rate limiting and usage caps to prevent runaway spend
  • Embed widget (script tag or iframe) for any site
  • Admin view of recent conversations for QA and improvement
  • Citations or source links when grounded on documents
  • Fallback to a human handoff for support use cases

Real-world examples

Docs Q&A bot

Embedded in your help center. Grounded on the docs site so answers cite the exact page — no hallucinated features.

Internal knowledge assistant

Behind project auth, hooked up to your team wiki and decision log. Cuts the 'where is X documented?' Slack thread to one minute.

Product onboarding coach

Walks new users through your product, answers setup questions, and surfaces relevant features at the right moment in the journey.

Why FloopFloop fits ai chatbot projects

AI chatbots have a deceptive build-vs-buy decision: every off-the-shelf tool (Intercom Fin, Chatbase, Voiceflow) charges a per-conversation or per-seat fee that adds up quickly once usage is real, and they own the data trail. Building from scratch means a sprint of plumbing — auth, key management, embeddings, the embed widget — before you write a single useful prompt. FloopFloop ships you to the useful-prompt step on day one. Describe the bot's job, paste your knowledge sources, plug in your own OpenAI/Anthropic key as a project secret, and iterate on the system prompt in conversation. Your conversations stay on your subdomain; your spend stays on your API key.

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.

관련 빌더

더 많은 카테고리 탐색

Knowledge Base

Build a searchable knowledge base with articles, categories, FAQ sections, and a help center layout — all generated from a prompt.

SaaS Analytics Dashboard

Build a SaaS analytics dashboard with MRR tracking, churn metrics, cohort analysis, and user funnels — generated entirely by AI.

Marketing Site

Build a multi-page marketing website with feature sections, pricing tables, team pages, and conversion-optimized CTAs — powered by AI.

Landing Page

Create a high-converting landing page with hero sections, testimonials, CTAs, and mobile-responsive design — all generated by AI.

CRM Dashboard

Build a customer relationship management dashboard with contact lists, deal pipelines, activity logs, and reporting — no coding required.

Admin Panel

Build a powerful admin panel with user management, data tables, role-based access, and system configuration — all generated from a prompt.

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