UrbanRide Assistant

In the digital marketplace, e-commerce businesses often struggle to provide personalized, interactive, and conversational product browsing experiences. Traditional websites present static product listings, which can overwhelm users with too much information at once.
Our project – UrbanRide Assistant – solves this by combining chat-based product discovery with visual image carousels. Users interact with the chatbot in natural language, receive relevant product recommendations, and view product images directly inside the chat. Most importantly, they can click on a “BUY” button under each image to instantly reach the product page — bridging conversational discovery with e-commerce action.

What Problem It Solves
• Overloaded product catalogs: Customers struggle to filter through large inventories. UrbanRide Assistant uses conversational queries to refine results.
• Lack of visual engagement in chatbots: Most bots return only text responses. UrbanRide Assistant uses image carousels to make browsing engaging and user-friendly.
• Disconnected shopping journey: Instead of sending plain links, UrbanRide Assistant integrates BUY button under product images, allowing smooth redirection to product pages while keeping the experience natural.
• Customer confidence: By confirming selections with a polite message (“Good choice, proceed to the product page for buying”), the bot reassures users and enhances trust.

Key Features
1. Conversational Filtering – Users can ask for bikes by size, color, category, or price range.
2. Dynamic Image Carousel – Returns a structured JSON response rendered as an interactive image carousel.
3. BUY Buttons - Clickable BUY button under each product image linked to the product page.
4. Confirmation Flow – After a user clicks “BUY,” the bot responds with a single confirmation and pauses until the user asks the next query.
5. Error Handling – If no results match, the bot gracefully responds with:
o “I dont have any answer”
o or “Sorry, no bikes match your request. Try another filter.”

Use Cases
• E-commerce Stores: Bike sellers, fashion retailers, electronics marketplaces.
• Customer Support: Chat-driven product recommendations.
• Marketing Campaigns: Showcase featured products with direct BUY links.

Future Enhancements
• Payment Gateway Integration: Complete the checkout within chat.
• Personalized Recommendations: Suggest products based on browsing history.
• Multi-category Carousels: Support for accessories, apparel, and spare parts.
• Analytics Dashboard: Track clicks on BUY links to measure engagement.

With this setup, UrbanRide Assistant not only demonstrates the potential of conversational commerce but also delivers a seamless bridge between chat-based product discovery and actual online purchases — creating a smoother, more engaging shopping experience.