AI-Powered E-commerce Bot: From Zero to Production
Build an AI-powered e-commerce customer support bot with LangGraph state machines, product search, order tracking, and Telegram integration.
The Problem with E-commerce Support
E-commerce businesses handle thousands of repetitive customer queries daily:
A human agent spends 70% of their time on these predictable questions. An AI bot can handle them instantly, 24/7, in any language.
Architecture: LangGraph State Machine
We use LangGraph to build a stateful conversation agent. Unlike simple prompt-response bots, a state machine tracks the conversation flow and can handle multi-step interactions.
`` from langgraph.graph import StateGraph, END class OrderState: messages: list order_id: str | None intent: str | None step: str graph = StateGraph(OrderState) graph.add_node("classify", classify_intent) graph.add_node("order_lookup", lookup_order) graph.add_node("product_search", search_products) graph.add_node("return_process", handle_return) graph.add_node("respond", generate_response)python
`
Intent Classification
First, classify what the customer wants:
`python
def classify_intent(state):
response = llm.invoke(f"""
Classify this customer message into one of:
- order_status, product_inquiry, return_request,
- complaint, general_question
Message: {state.messages[-1]}
""")
state.intent = response.strip()
return state
`
Order Tracking Integration
Connect to your e-commerce API:
`python
def lookup_order(state):
order = api.get_order(state.order_id)
state.context = {
"status": order.status,
"tracking": order.tracking_number,
"eta": order.estimated_delivery
}
return state
`
Telegram Integration
Deploy the bot on Telegram for instant customer access:
`python
from telegram import Update
from telegram.ext import Application, MessageHandler, filters
async def handle_message(update: Update, context):
user_msg = update.message.text
response = await agent.process(
user_id=update.effective_user.id,
message=user_msg
)
await update.message.reply_text(response)
app = Application.builder().token(BOT_TOKEN).build()
app.add_handler(MessageHandler(filters.TEXT, handle_message))
app.run_polling()
``
Key Features to Implement
1. Product Search with Semantic Understanding
Customer says "warm baby clothes for winter" → search returns relevant products even if they don't contain those exact words.
2. Multi-Language Support
Detect language automatically and respond in the same language. Essential for Turkish e-commerce with international customers.
3. Handoff to Human
When the bot can't resolve an issue, seamlessly transfer to a human agent with full conversation history.
4. Proactive Notifications
Send order status updates, delivery confirmations, and review requests automatically.
Results from Production
Our AI E-commerce Bot in production:
Get Started
Our AI E-commerce Bot package includes the complete LangGraph state machine, Telegram integration, product search with vector similarity, order tracking API connectors, and admin dashboard.
Starter ($49/mo) covers single-store deployment. Professional ($199/mo) includes multi-store, analytics, and custom training.