Building a WhatsApp AI Sales Agent with Next.js and Groq’s LLaMA

Rohit Sonar Blogs
Rohit Sonar
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In today’s world, customers expect instant replies especially when they message a business. But for small teams, it’s impossible to stay online 24/7. That’s where AI-powered sales agents step in.

I recently built a WhatsApp AI Sales Agent that automatically replies to customer messages on your WhatsApp Business number. It shares product information directly from a product catalog and answers questions conversationally using Groq’s LLaMA model.

Let’s break down how it works.

The Flow

  1. User sends a WhatsApp message to the business number.
  2. Meta’s WhatsApp Cloud API triggers a webhook to our backend endpoint (/api/webhook).
  3. On receiving the webhook:
    • Extracts the sender ID and message content (lib/whatsapp.ts).
    • Loads product catalog data from products.csv (lib/loadCsv.ts).
    • Builds a context prompt and queries Groq’s LLaMA model (lib/groq.ts).
    • The AI generates a reply.
    • The reply is sent back to the customer on WhatsApp (lib/whatsapp.ts).

This ensures customers instantly receive product info and answers—even outside business hours.

Key Components

  • Webhook Handler (app/api/webhook/route.ts)
    • Handles GET requests for Meta’s webhook verification (returns hub.challenge).
    • Handles POST requests for incoming messages, processes them, and returns replies.
  • WhatsApp Cloud API Helpers (lib/whatsapp.ts)
    • Extract sender and message fields.
    • Send AI-generated messages back to customers.
  • CSV Loader (lib/loadCsv.ts)
    • Reads products.csv.
    • Formats product details into a structured context for the AI.
  • AI Integration (lib/groq.ts)
    • Calls Groq’s LLaMA model.
    • Passes product data + user query as context.
    • Returns natural language answers tailored to the catalog.
  • Product Catalog (products.csv)
    • A simple CSV file containing product details.
    • Keeps the setup lightweight, scalable, and easy to update.

Configuration

Environment variables keep secrets and API keys secure:

  • WHATSAPP_TOKEN – WhatsApp API auth token
  • WHATSAPP_PHONE_ID – WhatsApp Business phone ID
  • WHATSAPP_VERIFY_TOKEN – Used during webhook verification
  • GROQ_API_KEY – Authentication for Groq’s LLaMA model
  • AI_SYSTEM_PROMPT (optional) – Custom system prompt for guiding AI responses

Deployment

The backend is built using Next.js API Routes and deployed on Vercel. This makes the system:

  • Serverless & Scalable – No servers to manage, scales automatically.
  • Lightweight – Just CSV + API calls, no complex database required at Stage 1.
  • Easily Extensible – Future features like authentication, analytics, or payments can be layered in.

Why This Matters

  • 24/7 Customer Engagement – Customers get immediate answers without waiting.
  • Reduced Workload – Teams don’t need to manually answer FAQs or product queries.
  • Scalable Foundation – The CSV-based MVP can later evolve into a full e-commerce backend.

Right now, this AI Sales Agent covers the basics: answering queries and sharing product info. There are lot of features that could be added to this project which i looking forward to .