Building Enterprise AI Chatbots with Next.js, Vercel AI SDK, and OpenAI

Building a chatbot is easy. Building an enterprise-grade, streaming, context-aware chatbot is hard. Here is how we build them at TelGates.

The Tech Stack

• Framework: Next.js (App Router)
• AI Tools: Vercel AI SDK, OpenAI API (GPT-4o)
• Database: PostgreSQL (Neon or Supabase) with pgvector for embeddings
• Styling: TailwindCSS + Radix UI

Core Features Required

• Real-time Streaming: Use `useChat` from Vercel AI SDK for instant responses without waiting for the full generation.
• Chat History: Store conversations in PostgreSQL to provide context across sessions.
• RAG Integration: Use pgvector to retrieve company knowledge before answering.
• Tool Calling: Allow the LLM to trigger actions (e.g., checking order status, booking meetings).

Implementing Tool Calling

With GPT-4o, you can define functions the model can call. For example, `getWeather(location)` or `checkInventory(productId)`. The LLM decides when to call these functions based on the user's intent, parses the JSON response, and generates a natural language reply.

Security Best Practices

• Implement rate limiting (Upstash Redis) to prevent API abuse.
• Never expose OpenAI keys on the client-side.
• Use system prompts to restrict the bot's domain (e.g., "You are a customer support agent. Do not answer questions outside of our product.").

Ready to Scale? Contact TelGates today to discover how our engineering team can build robust, scalable solutions tailored to your unique requirements.