Build an AI Tool in 21 Days
LLM integration, streaming, RAG, usage billing — production-grade AI product from day one.
Why AI Tool / LLM Product development is hard to get right
LLM API costs can spiral fast at scale — without usage limits and caching, margins collapse
Streaming responses look simple but require careful SSE/WebSocket architecture to not block the UI
RAG (Retrieval-Augmented Generation) with document upload is commonly requested and consistently underscoped
AI products need abuse prevention — rate limiting, content filtering, and prompt injection protection
What we build — in detail
LLM integration with streaming
OpenAI, Anthropic, or Gemini via Vercel AI SDK. Streaming response displayed word-by-word. Model switching without architecture changes.
RAG pipeline
Document upload (PDF, DOCX, TXT), chunking, embedding (OpenAI or local), vector storage in Supabase pgvector. Semantic retrieval feeds LLM context.
Conversation history
Full conversation persistence in Postgres. Thread-based UI. Share conversations. Export to PDF/markdown.
Usage limits per plan
Token counting, monthly credit system, or request limits per Stripe plan tier. Hard limits enforced at API level — not just UI.
Prompt management
System prompt configuration per use case. Prompt versioning for testing. A/B test different prompt strategies.
Rate limiting & abuse prevention
Upstash Redis rate limiting per user/IP. Content moderation via OpenAI moderation API. Prompt injection detection.
Cost monitoring
Token usage tracked per user, per request, per model. Cost dashboard so you can see margins in real-time and set alerts.
Tech Stack
- →Next.js 15
- →Vercel AI SDK
- →OpenAI / Anthropic / Gemini
- →Supabase + pgvector (RAG)
- →Upstash Redis (rate limiting)
- →Stripe Billing
- →Vercel
What you get
- Full-stack AI web application
- RAG pipeline (if applicable)
- Stripe usage billing
- Full source code
- Cost monitoring dashboard
Frequently asked questions
Which AI model should I use?
Depends on your use case. GPT-4o for general assistant tasks. Claude Sonnet 4 for long documents and nuanced reasoning. Gemini 2.5 Flash for high-volume, cost-sensitive tasks. We help you choose during scoping — and design the architecture to switch models easily.
How do I price my AI product without losing money?
API cost + 200–500% margin. GPT-4o costs ~$0.005 per typical request. At 500% margin, you charge $0.025/request — or $25/month for 1,000 requests. Use our API Cost Calculator to model your exact unit economics.
Can you build a RAG chatbot trained on my documents?
Yes — document upload, chunking, embedding, vector storage, and semantic retrieval is a standard pattern we implement. Add a chat interface and system prompt, and you have a document-aware AI assistant in ~1 week of build time.
Free AI Tool / LLM Product resources
Download these before you start — no credit card, just your email.
Ready to build your ai tool / llm product?
Book a free 30-minute scoping call. We'll map out exactly what v1 looks like, give you a fixed price, and start within a week.
Book a Free Scoping Call →Free · No commitment · Delivered in 2–3 weeks