Overview
A friendly, conversational storefront agentic experience that helps visitors complete bookings or purchases with confidence. It reduces friction in discovery and checkout and lowers the merchant support burden.
Smaller merchants often struggle with managing and servicing customer queries which often involves seemingly endless back‑and‑forth communication over phone or chat, which disrupts the customer journey and overloads support teams.
To address this, I built a RAG‑powered agentic assistant that retrieves merchant content from a vector index and generates concise, grounded replies in the user's language. The system links that conversational layer to UI components (product cards, variants, and prefilled forms) and uses natural‑language intent classification to route actions and produce AI responses, enabling bookings and purchases within a coherent conversation.
The result is a streamlined, integrated experience that surfaces factual answers, pre‑fills customer details, guides checkout, reduces repetitive support requests, and improves completion rates.
Agentic Commerce
The Agentic AI Assistant delivers a friendly, personalized, and intelligent conversational experience that helps visitors discover products, check availability, and complete purchases or bookings with confidence.
It interprets each customer’s intent, maintains context and state, and autonomously searches, compares, negotiates, and calls external functions (APIs/tools) as needed.
The assistant returns composable UI components and concise, context-aware responses that summarize status, show images/links, and suggest next actions—removing friction across discovery, checkout, and post‑purchase interactions to boost conversion and satisfaction.
Intent
Classification
Autonomous
Agents
Function
Calling
Context
Awareness
Component
Matching
Response
Generation
Suggested Actions
Context Preservation
This agentic commerce chatbot (Proof of Concept) handles complex queries by classifying them with predefined intents and invoking the appropriate tools to fetch relevant information.
Using context engineering for state management and conversation history, it becomes context-aware and delivers intelligent, personalized recommendations and suggestions.
The assistant supports free-flow, multi-turn conversations and presents results through modular visual components for a seamless, end-to-end shopping experience.