How a mid-market ecommerce retailer used AI consulting, generative AI, semantic product search, and personalization to answer shopper questions instantly, help customers find products faster, enrich catalog content at scale, and increase conversion and average order value.
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The client is an online retailer with more than 40,000 SKUs, roughly 2.5 million monthly sessions, and a growing mix of mobile and repeat customers. Growth had outpaced the team’s ability to keep search relevant, catalog content complete, and support response times low.
Teams worked across the ecommerce platform, ERP and order management, PIM, helpdesk, marketing tools, and analytics dashboards. Support agents answered the same questions repeatedly, merchandisers hand-tuned search and collections, and leaders had no unified view of what shoppers wanted but could not find.
JanBask designed and deployed SHOPIQ AI — an ecommerce AI solution combining semantic product search, personalized recommendations, a retrieval-augmented support assistant, generative catalog enrichment, and secure integrations with the commerce platform, ERP, and helpdesk. It helps shoppers find and buy faster, frees the support team for complex cases, and keeps merchandisers in control of the customer experience.
Understands natural-language, misspelled, and intent-based queries using semantic search, so shoppers searching for “waterproof hiking jacket under $150” see relevant products instead of empty or irrelevant results.
Blends browsing behavior, purchase history, and catalog relationships to recommend products on category, product, cart, and email touchpoints, lifting basket size without manual rule-building.
A retrieval-augmented assistant that answers order-status, shipping, returns, and product questions using live order data and approved policies, and hands complex cases to agents with the full context summarized.
Generates SEO-friendly product descriptions, titles, and attributes from supplier data and images, with brand-voice controls and human review, clearing the backlog of thin product pages.
Analyzes search terms, zero-result queries, conversion, and stock levels to show merchandisers which products to promote, restock, or fix before revenue is lost.
Connects approved data from the ecommerce platform, ERP and order management, PIM, helpdesk, and analytics tools to create a governed AI layer across the retailer’s existing technology stack.
Performance was measured against pre-implementation search, support, catalog, and checkout workflows, including contact deflection, search-to-cart conversion, content turnaround time, and average order value.
Get a free assessment of where AI can improve product search, personalization, support, and catalog content in your store.
An enterprise-grade AI and data architecture designed for ecommerce: semantic search, personalization, governed knowledge retrieval, catalog automation, secure order and customer data access, and scalable commerce operations. AI outputs were designed to support — not replace — support agents, merchandisers, and marketers, with human review on customer-facing content.
Ecommerce AI consulting helps online retailers and brands find the highest-value AI opportunities, then designs, builds, and governs the solution. Common projects include AI product search, personalization, support automation, catalog content generation, and demand forecasting.
AI improves conversion by making search understand shopper intent, recommending relevant products at every step, and answering questions before shoppers abandon. In this case study, search-to-cart conversion rose 27% and average order value rose 19%.
No. It resolves repetitive questions such as order status and returns eligibility, which cut support workload by 54%, and passes complex or sensitive cases to your agents with a summarized history so they can resolve them faster.
Yes. We integrate with leading commerce platforms, ERP and order management, PIM, and helpdesk systems through secure APIs, so the AI works with live catalog, inventory, and order data.
Most retailers see a working pilot within 8–12 weeks, typically starting with search or support automation, then expanding to personalization and catalog content once results are proven.
Answers are grounded in approved policies and live order data, monitored for accuracy, and escalated to humans when confidence is low. We apply role-based access, encryption, and audit logging, and customer data is never used to train public models.
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