Client project
LapVerse AI Shopping Assistant
A client AI shopping assistant project connecting a United States laptop storefront, product catalogue, and conversational assistant for needs-based recommendations and follow-up questions.
- Type
- Client project
- Status
- Client project
- Project type
- Client implementation
- Location
- United States
- Commercial detail
- Project fee: $1,259
- Relevant service
- AI Shopping Assistant
- Client identity
- LapVerse
- Published outcome
- Verified functional tests; no business impact measured
Approved project visual
LapVerse client-project visual showing the AI shopping assistant and product catalogue. No measured commercial results are claimed.
Business and problem
Context before automation.
Business
LapVerse is a client project for a laptop business in the United States. Rynx Systems connected its storefront, product catalogue, and conversational shopping assistant.
Problem
Catalogue filters narrow a product list, but they do not explain technical trade-offs or answer follow-up questions. LapVerse needed product guidance inside the shopping experience.
Solution and workflow
What can be described publicly.
Rynx Systems integrated a conversational AI assistant into the storefront, connected it to a supplied product catalogue, added session-based conversation memory, and validated recommendations before showing linked product cards.
- 01
The website sends the customer message, session identifier, and current product context to the backend.
- 02
Catalogue details, sample prices, and availability provide the recommendation context.
- 03
An n8n workflow combines the request, conversation history, and shopping instructions for OpenAI.
- 04
Validation checks product IDs, availability, and prices against the budget passed to the validation step.
- 05
The storefront displays a conversational response and linked product cards.
Key capabilities
Confirmed scope without invented claims.
Only details supported by the current brief are included. Unapproved implementation details remain private.
Session-based follow-up memory
Updated recommendations after budget changes
Current product-page context
Product cards with sample prices and links
Alternatives for unavailable products
Clear no-match responses
Next.js website, n8n workflows, OpenAI, webhooks, structured JSON responses, session-based conversation memory, and a supplied sample product catalogue.
A delivery timeline has not been approved for publication.
Testing demonstrated initial recommendations, follow-up questions about a shortlist, revised results after budget changes, alternatives for unavailable products, no-match responses, current-product questions, and linked recommendation cards. Sales uplift, conversion improvement, and support savings were not measured.
“Rynx Systems built a shopping assistant that helps customers find suitable products, compare options and ask follow-up questions directly on the website. The budget-based recommendations and clickable product cards make the shopping experience easier to navigate.” — Alex J, LapVerse
Relevant service
Explore AI Shopping Assistant for your workflow.
We’ll scope the role, information, boundaries, integrations, and handoff path around your actual requirement.