Conversational Shopping Assistant
An AI-powered shopping assistant that helps customers discover suitable products through natural conversation.
About the Project
CatalogPilot AI is a conversational shopping assistant designed for online stores with large or frequently changing product catalogs.
Customers can describe what they need, set a budget, and mention style or material preferences. The assistant searches live catalog databases and recommends suitable products with clear explanations on why they fit the shopper's needs.
The Problem & Objectives
Traditional category navigation and keyword search filters can make product discovery difficult, particularly when customers do not know the exact name of the item they want.
The assistant needed to understand conversational requests while keeping recommendations grounded in current inventory counts, active pricing tiers, and store policy files.
Identified Challenges
- [1]
Large Product Catalogs
Helping buyers narrow down hundreds of products to a relevant shortlist without clicking complex category filter lists.
- [2]
Frequently Changing Inventory
Ensuring recommendations reflect current stock levels, variations, and active pricing tables.
- [3]
Unclear Customer Intent
Understanding descriptions of occasions, styles, budgets, and materials instead of matching exact SKUs.
- [4]
Reliable Store Information
Providing accurate answers on return windows, shipping rates, and fees grounded in official policies.
How the Solution Was Designed
CatalogPilot connects the conversational agent to product database schemas, live inventory managers, and return/shipping policy sheets.
The system extracts user preferences such as target category, pricing range, styling terms, and material requests. It then retrieves matching products and presents a visual, relevant shortlist.
Product details come directly from the live catalog, while general customer support questions are answered using retrieval-augmented generation. When the assistant cannot resolve a request, it hands the conversation to customer support with the existing context.
Key Features Built into CatalogPilot
Core shopping helper features designed to link casual conversations directly with e-commerce cart checkouts safely.
Conversational Discovery
Shoppers find products using natural, descriptive phrases instead of keyword combinations.
Preference-Based Matching
Filters results based on budget limits, styles, materials, and occasion descriptions.
Live Catalog Integration
Pulls prices, stock statuses, and images directly from active storefront databases.
Store-Policy RAG Answers
Answers shipping, order tracking, and return questions using approved policy documents.
Conversational Cart Add
Enables buyers to add recommended variations to their digital shopping cart directly in chat.
Support Console Handoff
Routes complex ordering errors or shipping issues to human agents with full conversation summaries.
Smart Synonym Matching
Maps casual vocabulary (e.g. "cozy") to catalog attributes like fleece material.
Multi-Language Processing
Handles product queries and answers questions across multiple regional languages.
Conversation & Decision Flow
From a vague request to a resolved outcome.
Customer describes what they need
Natural language: budget, style, material, occasion, not exact product names.
Preference extraction
The agent parses category, price range, styling terms, and material requests.
Catalog, inventory & policy lookup
Matching products and store policy documents are retrieved live.
Resolved: add to cart
A matching product is found and the customer adds it directly from the conversation.
Unresolved: support handoff
If the request can't be resolved, it's handed to customer support with the existing context.
Technology Infrastructure
The frameworks and e-commerce APIs powering the CatalogPilot platform.
AI & Conversation
E-Commerce Sync
Data Store & Cache
Frontend Widget
Infrastructure
Need an intelligent shopping assistant built for your storefront?
Discuss your catalog size, ERP integrations, and conversational flows with our engineering team.