{"id":11562,"date":"2026-09-07T11:11:55","date_gmt":"2026-09-07T11:11:55","guid":{"rendered":"http:\/\/localhost\/Rysunmvplive\/?post_type=success-story&p=11562"},"modified":"2026-09-07T11:23:38","modified_gmt":"2026-09-07T11:23:38","slug":"ai-powered-conversational-commerce-assistant-accelerates-showroom-sales-for-a-premium-lighting-brand","status":"publish","type":"success-story","link":"http:\/\/phpdemo03.kcspl.in:9099\/rysunmvplive\/success-story\/ai-powered-conversational-commerce-assistant-accelerates-showroom-sales-for-a-premium-lighting-brand\/","title":{"rendered":"AI-Powered Conversational Commerce Assistant Accelerates Showroom Sales for a Premium Lighting Brand"},"content":{"rendered":"

[vc_row el_class=”casestudy-main-section”][vc_column][vc_row_inner el_class=”container animation fadeTop”][vc_column_inner][vc_custom_heading text=”Overview” use_theme_fonts=”yes” css=”” el_class=”custom-heading”][vc_column_text css=”” el_class=”common-para”]The client is a premium lighting brand with a large portfolio of decorative and architectural lighting products. Its showroom teams work with customers who often need detailed information about product specifications, availability, installation, warranties, pricing, orders, and after-sales services before making a purchase.<\/p>\n

As the product portfolio and supporting information grew, giving sales teams fast access to the right information during customer conversations became increasingly difficult.<\/p>\n

Rysun developed Chatter, an AI-powered Agentic Conversational Commerce Assistant that brings product and enterprise knowledge into a single conversational experience. [\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row el_class=”container”][vc_column][vc_row_inner el_class=”blueboxrow animation fadeTop casestudy-overview”][vc_column_inner el_class=”overview-block” width=”1\/3″][vc_single_image image=”3684″ img_size=”full” el_class=”overview-img”][vc_column_text el_class=”o-body-medium”]Industry\u200b[\/vc_column_text][vc_column_text css=”” el_class=”o-paragraph-medium”]Retail & Ecommerce[\/vc_column_text][\/vc_column_inner][vc_column_inner el_class=”overview-block” width=”1\/3″][vc_single_image image=”8449″ img_size=”full” css=”” el_class=”overview-img”][vc_column_text css=”” el_class=”o-body-medium”]Challenge[\/vc_column_text][vc_column_text css=”” el_class=”o-paragraph-medium”]Sales teams struggled to find accurate product and operational information quickly across a large catalog and multiple enterprise systems.[\/vc_column_text][\/vc_column_inner][vc_column_inner el_class=”overview-block” width=”1\/3″][vc_single_image image=”3686″ img_size=”full” el_class=”overview-img”][vc_column_text el_class=”o-body-medium”]Solution[\/vc_column_text][vc_column_text css=”” el_class=”o-paragraph-medium”]An AI-powered conversational commerce assistant that provides instant, grounded answers from a centralized enterprise knowledge base through voice or text.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row el_class=”casestudy-who”][vc_column][vc_row_inner el_class=”container animation fadeTop”][vc_column_inner el_class=”casestudy-who”][vc_custom_heading text=”Challenge” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes” css=”” el_class=”o-header–h3″][vc_column_text css=”” el_class=”common-para common-listing”]Sales associates were expected to work across a large and varied product catalog while keeping track of product specifications, stock availability, installation instructions, warranty conditions, service policies, and order information.<\/p>\n

Much of this information was distributed across internal applications, ERP systems, product data, PDFs, and other enterprise sources. Public search engines could not access it, while general-purpose AI tools did not have the internal business context or real-time enterprise data required to answer these questions accurately.<\/p>\n

Finding an answer during a customer conversation could therefore require searching several systems or asking another employee for help.<\/p>\n

This increased response times and made the quality of the showroom experience dependent on the individual associate\u2019s product knowledge and experience. It also made it harder for newer employees to become productive quickly across a broad product portfolio. [\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row el_class=”casestudy-bg-gradient mt-200″][vc_column][vc_row_inner el_class=”container animation fadeTop”][vc_column_inner el_class=”casestudy-who”][vc_custom_heading text=”Rysun\u2019s Solution” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes” css=”” el_class=”o-header–h4″][vc_column_text css=”” el_class=”common-para common-listing”]Rysun developed Chatter around a centralized enterprise knowledge layer that connects product information with the internal data sales teams need during customer interactions.<\/p>\n

Sales associates can ask questions in natural language through voice or text. Chatter retrieves relevant information from connected enterprise sources and returns a contextual response within the conversation.<\/p>\n

The solution brings together information such as:<\/p>\n